Framingham Heart Study: TenYearCHD classification:: template2
bdanalytics
Date: (Thu) Jun 25, 2015
Introduction:
Data: Source: Training: https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/framingham.csv
New:
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: First run:
Prediction Accuracy Enhancement Options:
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/framingham.csv"
glb_newdt_url <- "<newdt_url>"
glb_out_pfx <- "template2_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- FALSE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.35 # > 0 & < 1
glb_split_sample.seed <- 1000 # or any integer
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "TenYearCHD"
# for classification, the response variable has to be a factor
glb_rsp_var <- "TenYearCHD.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
# return(log(raw))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA))## [1] Y Y N N <NA>
## Levels: N Yglb_map_rsp_var_to_raw <- function(var) {
# return(exp(var))
as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA)))## [1] 1 1 0 0 NAif ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
glb_id_var <- NULL # or c("<var1>")
glb_category_vars <- NULL # or c("<var1>", "<var2>")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
# glb_derive_lst[["Week.bgn"]] <- list(
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# , args=c("Week"))
# require(zoo)
# # If glb_allobs_df is not sorted in the desired manner
# glb_derive_lst[["ILI.2.lag"]] <- list(
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# , args=c("Week"))
# glb_derive_lst[["ILI.2.lag"]] <- list(
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# , args=c("ILI"))
# glb_derive_lst[["ILI.2.lag.log"]] <- list(
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# , args=c("ILI.2.lag"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# Add logs of numerics that are not distributed normally -> do automatically ???
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# args_lst <- NULL; for (arg in glb_derive_lst[["Week.bgn"]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; do.call(mapfn, args_lst)
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- NULL # or c("<txt_var1>", "<txt_var2>")
#Sys.setlocale("LC_ALL", "C") # For english
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
#dsp_obs(Headline.contains="polit")
#subset(glb_allobs_df, H.T.compani > 0)[, c("UniqueID", "Headline", "H.T.compani")]
# glb_append_stop_words[["<txt_var1>"]] <- c(NULL
# # ,"<word1>" # <reason1>
# )
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_sprs_thresholds <- NULL # or c(0.988, 0.970, 0.970) # Generates 29, 22, 22 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# User-specified exclusions
glb_exclude_vars_as_features <- NULL # or c("<var_name>")
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # or TRUE
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL # or "<model_id_prefix>.<model_method>"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 7.369 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/framingham.csv..."
## [1] "dimensions of data in ./data/framingham.csv: 4,240 rows x 16 cols"
## male age education currentSmoker cigsPerDay BPMeds prevalentStroke
## 1 1 39 4 0 0 0 0
## 2 0 46 2 0 0 0 0
## 3 1 48 1 1 20 0 0
## 4 0 61 3 1 30 0 0
## 5 0 46 3 1 23 0 0
## 6 0 43 2 0 0 0 0
## prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose
## 1 0 0 195 106.0 70 26.97 80 77
## 2 0 0 250 121.0 81 28.73 95 76
## 3 0 0 245 127.5 80 25.34 75 70
## 4 1 0 225 150.0 95 28.58 65 103
## 5 0 0 285 130.0 84 23.10 85 85
## 6 1 0 228 180.0 110 30.30 77 99
## TenYearCHD
## 1 0
## 2 0
## 3 0
## 4 1
## 5 0
## 6 0
## male age education currentSmoker cigsPerDay BPMeds prevalentStroke
## 147 0 59 1 1 1 0 0
## 646 0 39 1 1 3 0 0
## 2160 0 42 3 1 15 0 0
## 3085 0 40 1 1 15 0 0
## 3116 1 44 1 0 0 0 0
## 4195 1 65 1 1 20 1 0
## prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose
## 147 1 0 259 141.0 86.0 25.97 70 86
## 646 0 0 273 116.0 86.0 28.73 75 NA
## 2160 0 0 212 115.0 72.0 23.72 73 100
## 3085 0 0 220 131.5 82.5 24.35 80 78
## 3116 0 0 238 132.0 86.0 27.22 75 85
## 4195 1 0 246 179.0 96.0 19.34 95 76
## TenYearCHD
## 147 0
## 646 0
## 2160 0
## 3085 0
## 3116 0
## 4195 1
## male age education currentSmoker cigsPerDay BPMeds prevalentStroke
## 4235 1 51 3 1 43 0 0
## 4236 0 48 2 1 20 NA 0
## 4237 0 44 1 1 15 0 0
## 4238 0 52 2 0 0 0 0
## 4239 1 40 3 0 0 0 0
## 4240 0 39 3 1 30 0 0
## prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose
## 4235 0 0 207 126.5 80 19.71 65 68
## 4236 0 0 248 131.0 72 22.00 84 86
## 4237 0 0 210 126.5 87 19.16 86 NA
## 4238 0 0 269 133.5 83 21.47 80 107
## 4239 1 0 185 141.0 98 25.60 67 72
## 4240 0 0 196 133.0 86 20.91 85 80
## TenYearCHD
## 4235 0
## 4236 0
## 4237 0
## 4238 0
## 4239 0
## 4240 0
## 'data.frame': 4240 obs. of 16 variables:
## $ male : int 1 0 1 0 0 0 0 0 1 1 ...
## $ age : int 39 46 48 61 46 43 63 45 52 43 ...
## $ education : int 4 2 1 3 3 2 1 2 1 1 ...
## $ currentSmoker : int 0 0 1 1 1 0 0 1 0 1 ...
## $ cigsPerDay : int 0 0 20 30 23 0 0 20 0 30 ...
## $ BPMeds : int 0 0 0 0 0 0 0 0 0 0 ...
## $ prevalentStroke: int 0 0 0 0 0 0 0 0 0 0 ...
## $ prevalentHyp : int 0 0 0 1 0 1 0 0 1 1 ...
## $ diabetes : int 0 0 0 0 0 0 0 0 0 0 ...
## $ totChol : int 195 250 245 225 285 228 205 313 260 225 ...
## $ sysBP : num 106 121 128 150 130 ...
## $ diaBP : num 70 81 80 95 84 110 71 71 89 107 ...
## $ BMI : num 27 28.7 25.3 28.6 23.1 ...
## $ heartRate : int 80 95 75 65 85 77 60 79 76 93 ...
## $ glucose : int 77 76 70 103 85 99 85 78 79 88 ...
## $ TenYearCHD : int 0 0 0 1 0 0 1 0 0 0 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## Loading required package: caTools## male age education currentSmoker cigsPerDay BPMeds prevalentStroke
## 2 0 46 2 0 0 0 0
## 5 0 46 3 1 23 0 0
## 9 1 52 1 0 0 0 0
## 14 0 41 3 0 0 1 0
## 17 1 48 3 1 10 0 0
## 19 0 38 2 1 5 0 0
## prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose
## 2 0 0 250 121.0 81.0 28.73 95 76
## 5 0 0 285 130.0 84.0 23.10 85 85
## 9 1 0 260 141.5 89.0 26.36 76 79
## 14 1 0 332 124.0 88.0 31.31 65 84
## 17 1 0 232 138.0 90.0 22.37 64 72
## 19 0 0 195 122.0 84.5 23.24 75 78
## TenYearCHD
## 2 0
## 5 0
## 9 0
## 14 0
## 17 0
## 19 0
## male age education currentSmoker cigsPerDay BPMeds prevalentStroke
## 411 1 36 3 0 0 0 0
## 1562 1 67 1 0 0 0 0
## 1670 0 47 1 0 0 0 0
## 2219 1 43 2 1 20 0 0
## 3713 0 42 2 0 0 0 0
## 3796 0 48 1 0 0 0 0
## prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose
## 411 0 0 174 131.0 86.0 26.23 75 64
## 1562 1 0 222 154.0 106.0 26.71 85 74
## 1670 1 0 305 128.0 92.5 27.64 75 62
## 2219 0 0 226 132.5 85.0 26.64 72 58
## 3713 0 0 234 124.0 80.5 20.06 75 NA
## 3796 1 0 224 152.5 90.0 29.80 67 85
## TenYearCHD
## 411 0
## 1562 0
## 1670 0
## 2219 0
## 3713 0
## 3796 0
## male age education currentSmoker cigsPerDay BPMeds prevalentStroke
## 4223 1 53 3 0 0 0 0
## 4225 1 47 2 1 3 0 0
## 4228 1 43 4 1 20 0 0
## 4229 0 50 1 0 0 0 0
## 4233 1 68 1 0 0 0 0
## 4236 0 48 2 1 20 NA 0
## prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose
## 4223 1 0 289 188.0 110 26.70 70 63
## 4225 0 0 198 120.0 80 25.23 75 76
## 4228 0 0 187 129.5 88 25.62 80 75
## 4229 1 1 260 190.0 130 43.67 85 260
## 4233 1 0 176 168.0 97 23.14 60 79
## 4236 0 0 248 131.0 72 22.00 84 86
## TenYearCHD
## 4223 0
## 4225 0
## 4228 0
## 4229 0
## 4233 1
## 4236 0
## 'data.frame': 1484 obs. of 16 variables:
## $ male : int 0 0 1 0 1 0 0 1 1 0 ...
## $ age : int 46 46 52 41 48 38 60 43 37 41 ...
## $ education : int 2 3 1 3 3 2 1 4 2 2 ...
## $ currentSmoker : int 0 1 0 0 1 1 0 1 0 1 ...
## $ cigsPerDay : int 0 23 0 0 10 5 0 43 0 1 ...
## $ BPMeds : int 0 0 0 1 0 0 0 0 0 0 ...
## $ prevalentStroke: int 0 0 0 0 0 0 0 0 0 0 ...
## $ prevalentHyp : int 0 0 1 1 1 0 0 0 1 0 ...
## $ diabetes : int 0 0 0 0 0 0 0 0 0 0 ...
## $ totChol : int 250 285 260 332 232 195 260 226 225 237 ...
## $ sysBP : num 121 130 142 124 138 ...
## $ diaBP : num 81 84 89 88 90 84.5 72.5 85.5 92.5 78 ...
## $ BMI : num 28.7 23.1 26.4 31.3 22.4 ...
## $ heartRate : int 95 85 76 65 64 75 65 75 95 75 ...
## $ glucose : int 76 85 79 84 72 78 NA 75 83 74 ...
## $ TenYearCHD : int 0 0 0 0 0 0 0 0 0 0 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## male age education currentSmoker cigsPerDay BPMeds prevalentStroke
## 1 1 39 4 0 0 0 0
## 3 1 48 1 1 20 0 0
## 4 0 61 3 1 30 0 0
## 6 0 43 2 0 0 0 0
## 7 0 63 1 0 0 0 0
## 8 0 45 2 1 20 0 0
## prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose
## 1 0 0 195 106.0 70 26.97 80 77
## 3 0 0 245 127.5 80 25.34 75 70
## 4 1 0 225 150.0 95 28.58 65 103
## 6 1 0 228 180.0 110 30.30 77 99
## 7 0 0 205 138.0 71 33.11 60 85
## 8 0 0 313 100.0 71 21.68 79 78
## TenYearCHD
## 1 0
## 3 0
## 4 1
## 6 0
## 7 1
## 8 0
## male age education currentSmoker cigsPerDay BPMeds prevalentStroke
## 12 0 43 2 0 0 0 0
## 104 0 48 1 0 0 0 0
## 1010 0 51 2 1 1 0 0
## 2482 0 45 2 0 0 0 0
## 2674 1 36 1 1 20 0 0
## 3205 1 45 1 0 0 0 0
## prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose
## 12 0 0 247 131 88.0 27.64 72 61
## 104 1 0 265 145 77.0 24.23 74 64
## 1010 0 0 220 142 82.5 21.02 56 78
## 2482 0 0 260 98 74.0 19.16 73 76
## 2674 0 0 242 115 75.0 25.64 83 83
## 3205 0 0 275 105 86.0 32.92 75 92
## TenYearCHD
## 12 0
## 104 0
## 1010 0
## 2482 0
## 2674 0
## 3205 0
## male age education currentSmoker cigsPerDay BPMeds prevalentStroke
## 4234 1 50 1 1 1 0 0
## 4235 1 51 3 1 43 0 0
## 4237 0 44 1 1 15 0 0
## 4238 0 52 2 0 0 0 0
## 4239 1 40 3 0 0 0 0
## 4240 0 39 3 1 30 0 0
## prevalentHyp diabetes totChol sysBP diaBP BMI heartRate glucose
## 4234 1 0 313 179.0 92 25.97 66 86
## 4235 0 0 207 126.5 80 19.71 65 68
## 4237 0 0 210 126.5 87 19.16 86 NA
## 4238 0 0 269 133.5 83 21.47 80 107
## 4239 1 0 185 141.0 98 25.60 67 72
## 4240 0 0 196 133.0 86 20.91 85 80
## TenYearCHD
## 4234 1
## 4235 0
## 4237 0
## 4238 0
## 4239 0
## 4240 0
## 'data.frame': 2756 obs. of 16 variables:
## $ male : int 1 1 0 0 0 0 1 0 0 1 ...
## $ age : int 39 48 61 43 63 45 43 50 43 46 ...
## $ education : int 4 1 3 2 1 2 1 1 2 1 ...
## $ currentSmoker : int 0 1 1 0 0 1 1 0 0 1 ...
## $ cigsPerDay : int 0 20 30 0 0 20 30 0 0 15 ...
## $ BPMeds : int 0 0 0 0 0 0 0 0 0 0 ...
## $ prevalentStroke: int 0 0 0 0 0 0 0 0 0 0 ...
## $ prevalentHyp : int 0 0 1 1 0 0 1 0 0 1 ...
## $ diabetes : int 0 0 0 0 0 0 0 0 0 0 ...
## $ totChol : int 195 245 225 228 205 313 225 254 247 294 ...
## $ sysBP : num 106 128 150 180 138 ...
## $ diaBP : num 70 80 95 110 71 71 107 76 88 94 ...
## $ BMI : num 27 25.3 28.6 30.3 33.1 ...
## $ heartRate : int 80 75 65 77 60 79 93 75 72 98 ...
## $ glucose : int 77 70 103 99 85 78 88 76 61 64 ...
## $ TenYearCHD : int 0 0 1 0 1 0 0 0 0 0 ...
## - attr(*, "comment")= chr "glb_trnobs_df"if ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}## Warning: using .rownames as identifiers for observationsif (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 7.369 7.882 0.513
## 2 inspect.data 2 0 7.882 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## Loading required package: reshape2
## TenYearCHD.0 TenYearCHD.1
## Test 1259 225
## Train 2337 419
## TenYearCHD.0 TenYearCHD.1
## Test 0.8483827 0.1516173
## Train 0.8479681 0.1520319
## [1] "numeric data missing in glb_allobs_df: "
## education cigsPerDay BPMeds totChol BMI heartRate
## 105 29 53 50 19 1
## glucose
## 388
## [1] "numeric data w/ 0s in glb_allobs_df: "
## male currentSmoker cigsPerDay BPMeds
## 2420 2145 2145 4063
## prevalentStroke prevalentHyp diabetes TenYearCHD
## 4215 2923 4131 3596
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## .rownames
## 0# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## TenYearCHD TenYearCHD.fctr .n
## 1 0 N 3596
## 2 1 Y 644
## TenYearCHD.fctr.N TenYearCHD.fctr.Y
## Test 1259 225
## Train 2337 419
## TenYearCHD.fctr.N TenYearCHD.fctr.Y
## Test 0.8483827 0.1516173
## Train 0.8479681 0.1520319# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: male"
## [1] "feat: age"
## [1] "feat: education"## Warning: Removed 105 rows containing non-finite values (stat_boxplot).## Warning: Removed 105 rows containing missing values (stat_summary).
## [1] "feat: currentSmoker"
## [1] "feat: cigsPerDay"## Warning: Removed 29 rows containing non-finite values (stat_boxplot).## Warning: Removed 29 rows containing missing values (stat_summary).
## [1] "feat: BPMeds"## Warning: Removed 53 rows containing non-finite values (stat_boxplot).## Warning: Removed 53 rows containing missing values (stat_summary).
## [1] "feat: prevalentStroke"
## [1] "feat: prevalentHyp"
## [1] "feat: diabetes"
## [1] "feat: totChol"## Warning: Removed 50 rows containing non-finite values (stat_boxplot).## Warning: Removed 50 rows containing missing values (stat_summary).
## [1] "feat: sysBP"
## [1] "feat: diaBP"
## [1] "feat: BMI"## Warning: Removed 19 rows containing non-finite values (stat_boxplot).## Warning: Removed 19 rows containing missing values (stat_summary).
## [1] "feat: heartRate"## Warning: Removed 1 rows containing non-finite values (stat_boxplot).## Warning: Removed 1 rows containing missing values (stat_summary).
## [1] "feat: glucose"## Warning: Removed 388 rows containing non-finite values (stat_boxplot).## Warning: Removed 388 rows containing missing values (stat_summary).
## [1] "feat: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 7.882 17.703 9.822
## 3 scrub.data 2 1 17.704 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## education cigsPerDay BPMeds totChol BMI heartRate
## 105 29 53 50 19 1
## glucose
## 388
## [1] "numeric data w/ 0s in glb_allobs_df: "
## male currentSmoker cigsPerDay BPMeds
## 2420 2145 2145 4063
## prevalentStroke prevalentHyp diabetes TenYearCHD
## 4215 2923 4131 3596
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## .rownames
## 0dsp_catgs <- function() {
print("NewsDesk:")
print(table(glb_allobs_df$NewsDesk))
print("SectionName:")
print(table(glb_allobs_df$SectionName))
print("SubsectionName:")
print(table(glb_allobs_df$SubsectionName))
}
# sel_obs <- function(Popular=NULL,
# NewsDesk=NULL, SectionName=NULL, SubsectionName=NULL,
# Headline.contains=NULL, Snippet.contains=NULL, Abstract.contains=NULL,
# Headline.pfx=NULL, NewsDesk.nb=NULL, .clusterid=NULL, myCategory=NULL,
# perl=FALSE) {
sel_obs <- function(vars_lst) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
if (!is.null(Popular)) {
if (is.na(Popular))
tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
}
if (!is.null(NewsDesk))
tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
if (!is.null(SectionName))
tmp_df <- tmp_df[tmp_df$SectionName == SectionName, ]
if (!is.null(SubsectionName))
tmp_df <- tmp_df[tmp_df$SubsectionName == SubsectionName, ]
if (!is.null(Headline.contains))
tmp_df <-
tmp_df[grep(Headline.contains, tmp_df$Headline, perl=perl), ]
if (!is.null(Snippet.contains))
tmp_df <-
tmp_df[grep(Snippet.contains, tmp_df$Snippet, perl=perl), ]
if (!is.null(Abstract.contains))
tmp_df <-
tmp_df[grep(Abstract.contains, tmp_df$Abstract, perl=perl), ]
if (!is.null(Headline.pfx)) {
if (length(grep("Headline.pfx", names(tmp_df), fixed=TRUE, value=TRUE))
> 0) tmp_df <-
tmp_df[tmp_df$Headline.pfx == Headline.pfx, ] else
warning("glb_allobs_df does not contain Headline.pfx; ignoring that filter")
}
if (!is.null(NewsDesk.nb)) {
if (any(grepl("NewsDesk.nb", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$NewsDesk.nb == NewsDesk.nb, ] else
warning("glb_allobs_df does not contain NewsDesk.nb; ignoring that filter")
}
if (!is.null(.clusterid)) {
if (any(grepl(".clusterid", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$clusterid == clusterid, ] else
warning("glb_allobs_df does not contain clusterid; ignoring that filter") }
if (!is.null(myCategory)) {
if (!(myCategory %in% names(glb_allobs_df)))
tmp_df <-
tmp_df[tmp_df$myCategory == myCategory, ] else
warning("glb_allobs_df does not contain myCategory; ignoring that filter")
}
return(glb_allobs_df$UniqueID %in% tmp_df$UniqueID)
}
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
union(c("UniqueID", "Popular", "myCategory", "Headline"), cols), FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_tbl <- function(...) {
tmp_entity_df <- glb_allobs_df[sel_obs(...), ]
tmp_tbl <- table(tmp_entity_df$NewsDesk,
tmp_entity_df$SectionName,
tmp_entity_df$SubsectionName,
tmp_entity_df$Popular, useNA="ifany")
#print(names(tmp_tbl))
#print(dimnames(tmp_tbl))
print(tmp_tbl)
}
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Create myCategory <- NewsDesk#SectionName#SubsectionName
# Fix some data before merging categories
# glb_allobs_df[sel_obs(Headline.contains="Your Turn:", NewsDesk=""),
# "NewsDesk"] <- "Styles"
# glb_allobs_df[sel_obs(Headline.contains="School", NewsDesk="", SectionName="U.S.",
# SubsectionName=""),
# "SubsectionName"] <- "Education"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SectionName"] <- "Business Day"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SubsectionName"] <- "Small Business"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SectionName"] <- "Opinion"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SubsectionName"] <- "Room For Debate"
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName="", Popular=NA),
# "SubsectionName"] <- "Small Business"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(7973),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName=""),
# "SectionName"] <- "Technology"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5076, 5736, 5924, 5911, 6532),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(SectionName="Health"),
# "NewsDesk"] <- "Science"
# glb_allobs_df[sel_obs(SectionName="Travel"),
# "NewsDesk"] <- "Travel"
#
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SectionName"] <- ""
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SubsectionName"] <- ""
# glb_allobs_df[sel_obs(NewsDesk="Styles", SectionName="", SubsectionName="", Popular=1),
# "SectionName"] <- "U.S."
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5486),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df$myCategory <- paste(glb_allobs_df$NewsDesk,
# glb_allobs_df$SectionName,
# glb_allobs_df$SubsectionName,
# sep="#")
# dsp_obs( Headline.contains="Music:"
# #,NewsDesk=""
# #,SectionName=""
# #,SubsectionName="Fashion & Style"
# #,Popular=1 #NA
# ,cols= c("UniqueID", "Headline", "Popular", "myCategory",
# "NewsDesk", "SectionName", "SubsectionName"),
# all=TRUE)
# dsp_obs( Headline.contains="."
# ,NewsDesk=""
# ,SectionName="Opinion"
# ,SubsectionName=""
# #,Popular=1 #NA
# ,cols= c("UniqueID", "Headline", "Popular", "myCategory",
# "NewsDesk", "SectionName", "SubsectionName"),
# all=TRUE)
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "#Crosswords/Games#" = "Business#Crosswords/Games#",
# "Business##" = "Business#Technology#",
# "#Open#" = "Business#Technology#",
# "#Technology#" = "Business#Technology#",
#
# "#Arts#" = "Culture#Arts#",
# "Culture##" = "Culture#Arts#",
#
# "#World#Asia Pacific" = "Foreign#World#Asia Pacific",
# "Foreign##" = "Foreign#World#",
#
# "#N.Y. / Region#" = "Metro#N.Y. / Region#",
#
# "#Opinion#" = "OpEd#Opinion#",
# "OpEd##" = "OpEd#Opinion#",
#
# "#Health#" = "Science#Health#",
# "Science##" = "Science#Health#",
#
# "Styles##" = "Styles##Fashion",
# "Styles#Health#" = "Science#Health#",
# "Styles#Style#Fashion & Style" = "Styles##Fashion",
#
# "#Travel#" = "Travel#Travel#",
#
# "Magazine#Magazine#" = "myOther",
# "National##" = "myOther",
# "National#U.S.#Politics" = "myOther",
# "Sports##" = "myOther",
# "Sports#Sports#" = "myOther",
# "#U.S.#" = "myOther",
#
#
# # "Business##Small Business" = "Business#Business Day#Small Business",
# #
# # "#Opinion#" = "#Opinion#Room For Debate",
# "##" = "##"
# # "Business##" = "Business#Business Day#Dealbook",
# # "Foreign#World#" = "Foreign##",
# # "#Open#" = "Other",
# # "#Opinion#The Public Editor" = "OpEd#Opinion#",
# # "Styles#Health#" = "Styles##",
# # "Styles#Style#Fashion & Style" = "Styles##",
# # "#U.S.#" = "#U.S.#Education",
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
# Copy Headline into Snipper & Abstract if they are empty
# print(glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, c("Headline", "Snippet")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Snippet,
# c("UniqueID", "Headline", "Snippet")])
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Snippet"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Headline"]
#
# print(glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, c("Headline", "Abstract")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Abstract,
# c("UniqueID", "Headline", "Abstract")])
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Abstract"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Headline"]
# WordCount_0_df <- subset(glb_allobs_df, WordCount == 0)
# table(WordCount_0_df$Popular, WordCount_0_df$WordCount, useNA="ifany")
# myprint_df(WordCount_0_df[,
# c("UniqueID", "Popular", "WordCount", "Headline")])Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 17.704 19.657 1.953
## 4 transform.data 2 2 19.657 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 19.657 19.717 0.06
## 5 extract.features 3 0 19.717 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 19.723 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 19.723 19.737
## 2 extract.features_factorize.str.vars 2 0 19.738 NA
## elapsed
## 1 0.014
## 2 NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## .src .rownames
## ".src" ".rownames"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}
if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
glb_txt_map_df <- read.csv("mytxt_map.csv", comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[163, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, tolower)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation,
preserve_intra_word_dashes=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
filter_df <- read.csv("mytxt_compound.csv", comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, tolower) #nuppr
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
# Not to be run in production
inspect_terms <- function() {
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full +term, full_Tf_df)
print(myplot_histogram(full_Tf_df, "Tf.full"))
myprint_df(full_Tf_df)
#txt_corpus[[which(grepl("zun", txt_vctr, ignore.case=TRUE))]]
digit_terms_df <- subset(full_Tf_df, grepl("[[:digit:]]", term))
myprint_df(digit_terms_df)
return(full_Tf_df)
}
#print("RemovePunct:"); remove_punct_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english"))) #nstopwrds
#print("StoppedWords:"); stopped_words_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, stemDocument) #Features for lost information: Difference/ratio in density of full_TfIdf_DTM ???
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_Tf_df <- inspect_terms()
#stemmed_stopped_Tf_df <- merge(stemmed_words_Tf_df, stopped_words_Tf_df, by="term", all=TRUE, suffixes=c(".stem", ".stop"))
#myprint_df(stemmed_stopped_Tf_df)
#print(subset(stemmed_stopped_Tf_df, grepl("compan", term)))
#glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
#grep("scene", names(full_TfIdf_vctr), value=TRUE)
#which.max(full_TfIdf_mtrx[, "yearlong"])
full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
names(full_TfIdf_df) <- "TfIdf.full"
full_TfIdf_df$term <- rownames(full_TfIdf_df)
full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
names(sprs_TfIdf_df) <- "TfIdf.sprs"
sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
# plt_X_df <- cbind(txt_X_df, glb_allobs_df[, c(glb_id_var, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today", xcol_name=glb_rsp_var))
# log_X_df <- log(1 + txt_X_df)
# colnames(log_X_df) <- paste(colnames(txt_X_df), ".log", sep="")
# plt_X_df <- cbind(log_X_df, glb_allobs_df[, c(glb_id_var, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today.log", xcol_name=glb_rsp_var))
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#sav_allobs_df <- glb_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
#txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_DTM' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_vctr' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'glb_full_DTM_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'glb_sprs_DTM_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'txt_corpus' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'txt_vctr' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 19.738 19.755
## 3 extract.features_end 3 0 19.756 NA
## elapsed
## 2 0.017
## 3 NAmyplt_chunk(extract.features_chunk_df)## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 19.738 19.755
## 1 extract.features_bgn 1 0 19.723 19.737
## elapsed duration
## 2 0.017 0.017
## 1 0.014 0.014
## [1] "Total Elapsed Time: 19.755 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 19.717 21.03 1.313
## 6 cluster.data 4 0 21.031 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 6 cluster.data 4 0 21.031 21.321 0.29
## 7 manage.missing.data 4 1 21.321 NA NA# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## education cigsPerDay BPMeds totChol BMI heartRate
## 105 29 53 50 19 1
## glucose
## 388
## [1] "numeric data w/ 0s in glb_allobs_df: "
## male currentSmoker cigsPerDay BPMeds
## 2420 2145 2145 4063
## prevalentStroke prevalentHyp diabetes TenYearCHD
## 4215 2923 4131 3596
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## .rownames
## 0# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
# complete(mice()) changes attributes of factors even though values don't change
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col], inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
} ## education cigsPerDay BPMeds totChol BMI heartRate
## 105 29 53 50 19 1
## glucose
## 388## Loading required package: mice
## Loading required package: Rcpp
## mice 2.22 2014-06-10## [1] "Summary before imputation: "
## male age education currentSmoker
## Min. :0.0000 Min. :32.00 Min. :1.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:42.00 1st Qu.:1.000 1st Qu.:0.0000
## Median :0.0000 Median :49.00 Median :2.000 Median :0.0000
## Mean :0.4292 Mean :49.58 Mean :1.979 Mean :0.4941
## 3rd Qu.:1.0000 3rd Qu.:56.00 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :70.00 Max. :4.000 Max. :1.0000
## NA's :105
## cigsPerDay BPMeds prevalentStroke prevalentHyp
## Min. : 0.000 Min. :0.00000 Min. :0.000000 Min. :0.0000
## 1st Qu.: 0.000 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.0000
## Median : 0.000 Median :0.00000 Median :0.000000 Median :0.0000
## Mean : 9.006 Mean :0.02962 Mean :0.005896 Mean :0.3106
## 3rd Qu.:20.000 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:1.0000
## Max. :70.000 Max. :1.00000 Max. :1.000000 Max. :1.0000
## NA's :29 NA's :53
## diabetes totChol sysBP diaBP
## Min. :0.00000 Min. :107.0 Min. : 83.5 Min. : 48.0
## 1st Qu.:0.00000 1st Qu.:206.0 1st Qu.:117.0 1st Qu.: 75.0
## Median :0.00000 Median :234.0 Median :128.0 Median : 82.0
## Mean :0.02571 Mean :236.7 Mean :132.4 Mean : 82.9
## 3rd Qu.:0.00000 3rd Qu.:263.0 3rd Qu.:144.0 3rd Qu.: 90.0
## Max. :1.00000 Max. :696.0 Max. :295.0 Max. :142.5
## NA's :50
## BMI heartRate glucose .rnorm
## Min. :15.54 Min. : 44.00 Min. : 40.00 Min. :-3.561788
## 1st Qu.:23.07 1st Qu.: 68.00 1st Qu.: 71.00 1st Qu.:-0.688598
## Median :25.40 Median : 75.00 Median : 78.00 Median :-0.002425
## Mean :25.80 Mean : 75.88 Mean : 81.96 Mean :-0.000141
## 3rd Qu.:28.04 3rd Qu.: 83.00 3rd Qu.: 87.00 3rd Qu.: 0.674799
## Max. :56.80 Max. :143.00 Max. :394.00 Max. : 3.739140
## NA's :19 NA's :1 NA's :388
##
## iter imp variable
## 1 1 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 1 2 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 1 3 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 1 4 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 1 5 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 2 1 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 2 2 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 2 3 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 2 4 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 2 5 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 3 1 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 3 2 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 3 3 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 3 4 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 3 5 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 4 1 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 4 2 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 4 3 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 4 4 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 4 5 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 5 1 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 5 2 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 5 3 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 5 4 education cigsPerDay BPMeds totChol BMI heartRate glucose
## 5 5 education cigsPerDay BPMeds totChol BMI heartRate glucose
## male age education currentSmoker
## Min. :0.0000 Min. :32.00 Min. :1.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:42.00 1st Qu.:1.000 1st Qu.:0.0000
## Median :0.0000 Median :49.00 Median :2.000 Median :0.0000
## Mean :0.4292 Mean :49.58 Mean :1.982 Mean :0.4941
## 3rd Qu.:1.0000 3rd Qu.:56.00 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :70.00 Max. :4.000 Max. :1.0000
## cigsPerDay BPMeds prevalentStroke prevalentHyp
## Min. : 0.00 Min. :0.00000 Min. :0.000000 Min. :0.0000
## 1st Qu.: 0.00 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.0000
## Median : 0.00 Median :0.00000 Median :0.000000 Median :0.0000
## Mean : 9.05 Mean :0.02995 Mean :0.005896 Mean :0.3106
## 3rd Qu.:20.00 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:1.0000
## Max. :70.00 Max. :1.00000 Max. :1.000000 Max. :1.0000
## diabetes totChol sysBP diaBP
## Min. :0.00000 Min. :107.0 Min. : 83.5 Min. : 48.0
## 1st Qu.:0.00000 1st Qu.:206.0 1st Qu.:117.0 1st Qu.: 75.0
## Median :0.00000 Median :234.0 Median :128.0 Median : 82.0
## Mean :0.02571 Mean :236.7 Mean :132.4 Mean : 82.9
## 3rd Qu.:0.00000 3rd Qu.:263.0 3rd Qu.:144.0 3rd Qu.: 90.0
## Max. :1.00000 Max. :696.0 Max. :295.0 Max. :142.5
## BMI heartRate glucose .rnorm
## Min. :15.54 Min. : 44.00 Min. : 40.00 Min. :-3.561788
## 1st Qu.:23.06 1st Qu.: 68.00 1st Qu.: 71.00 1st Qu.:-0.688598
## Median :25.38 Median : 75.00 Median : 78.00 Median :-0.002425
## Mean :25.79 Mean : 75.88 Mean : 81.71 Mean :-0.000141
## 3rd Qu.:28.04 3rd Qu.: 83.00 3rd Qu.: 87.00 3rd Qu.: 0.674799
## Max. :56.80 Max. :143.00 Max. :394.00 Max. : 3.739140mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in glb_allobs_df: "
## education cigsPerDay BPMeds totChol BMI heartRate
## 105 29 53 50 19 1
## glucose
## 388
## [1] "numeric data w/ 0s in glb_allobs_df: "
## male currentSmoker cigsPerDay BPMeds
## 2420 2145 2145 4063
## prevalentStroke prevalentHyp diabetes TenYearCHD
## 4215 2923 4131 3596
## cigsPerDay.nonNA BPMeds.nonNA
## 2145 4113
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## .rownames
## 0Step 4.1: manage missing data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stophere; sav_allobs_df <- glb_allobs_df;
print("Clustering features: ")
print(cluster_vars <- grep("[HSA]\\.[PT]\\.", names(glb_allobs_df), value=TRUE))
#print(cluster_vars <- grep("[HSA]\\.", names(glb_allobs_df), value=TRUE))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (myCategory in c("##", "Business#Business Day#Dealbook", "OpEd#Opinion#",
"Styles#U.S.#", "Business#Technology#", "Science#Health#",
"Culture#Arts#")) {
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df$myCategory == myCategory, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df$myCategory.fctr) / minClusterSize=20)
# which(levels(glb_allobs_df$myCategory.fctr) == myCategory)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df$myCategory==myCategory,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_allobs_df,
c("myCategory", ".clusterid", glb_rsp_var)))
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + .clusterid ~
Popular.fctr, sum, value.var=".n"))
print(ctgry_cast_df)
#print(orderBy(~ myCategory -Y -NA, ctgry_cast_df))
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_clst.csv"),
# row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df$.clusterid,
glb_allobs_df[, glb_rsp_var],
useNA="ifany"))
# dsp_obs(.clusterid=1, myCategory="OpEd#Opinion#",
# cols=c("UniqueID", "Popular", "myCategory", ".clusterid", "Headline"),
# all=TRUE)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features["myCategory.fctr"] <- c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 manage.missing.data 4 1 21.321 30.357 9.036
## 8 select.features 5 0 30.357 NA NAStep 5.0: select features
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## id cor.y exclude.as.feat cor.y.abs
## TenYearCHD TenYearCHD 1.000000000 1 1.000000000
## age age 0.226307320 0 0.226307320
## sysBP sysBP 0.208178303 0 0.208178303
## prevalentHyp prevalentHyp 0.178624122 0 0.178624122
## diaBP diaBP 0.135980635 0 0.135980635
## glucose glucose 0.125905881 1 0.125905881
## glucose.nonNA glucose.nonNA 0.117693791 0 0.117693791
## totChol.nonNA totChol.nonNA 0.101569483 0 0.101569483
## totChol totChol 0.101114643 1 0.101114643
## BPMeds BPMeds 0.094314490 1 0.094314490
## BPMeds.nonNA BPMeds.nonNA 0.092269768 0 0.092269768
## male male 0.085680208 0 0.085680208
## BMI BMI 0.082769510 1 0.082769510
## BMI.nonNA BMI.nonNA 0.080208915 0 0.080208915
## prevalentStroke prevalentStroke 0.078578145 0 0.078578145
## diabetes diabetes 0.077858385 0 0.077858385
## education education -0.058939008 1 0.058939008
## education.nonNA education.nonNA -0.057873618 0 0.057873618
## cigsPerDay cigsPerDay 0.046633660 1 0.046633660
## cigsPerDay.nonNA cigsPerDay.nonNA 0.044731673 0 0.044731673
## .rnorm .rnorm -0.019000943 0 0.019000943
## currentSmoker currentSmoker 0.012560421 0 0.012560421
## heartRate heartRate 0.007677700 1 0.007677700
## heartRate.nonNA heartRate.nonNA 0.006531896 0 0.006531896# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(diaBP, sysBP)=0.7768"
## [1] "cor(TenYearCHD.fctr, diaBP)=0.1360"
## [1] "cor(TenYearCHD.fctr, sysBP)=0.2082"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified diaBP as highly correlated with sysBP## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 22 TenYearCHD 1.000000000 1 1.000000000 <NA>
## 2 age 0.226307320 0 0.226307320 <NA>
## 21 sysBP 0.208178303 0 0.208178303 <NA>
## 19 prevalentHyp 0.178624122 0 0.178624122 <NA>
## 11 diaBP 0.135980635 0 0.135980635 sysBP
## 14 glucose 0.125905881 1 0.125905881 <NA>
## 15 glucose.nonNA 0.117693791 0 0.117693791 <NA>
## 24 totChol.nonNA 0.101569483 0 0.101569483 <NA>
## 23 totChol 0.101114643 1 0.101114643 <NA>
## 5 BPMeds 0.094314490 1 0.094314490 <NA>
## 6 BPMeds.nonNA 0.092269768 0 0.092269768 <NA>
## 18 male 0.085680208 0 0.085680208 <NA>
## 3 BMI 0.082769510 1 0.082769510 <NA>
## 4 BMI.nonNA 0.080208915 0 0.080208915 <NA>
## 20 prevalentStroke 0.078578145 0 0.078578145 <NA>
## 10 diabetes 0.077858385 0 0.077858385 <NA>
## 7 cigsPerDay 0.046633660 1 0.046633660 <NA>
## 8 cigsPerDay.nonNA 0.044731673 0 0.044731673 <NA>
## 9 currentSmoker 0.012560421 0 0.012560421 <NA>
## 16 heartRate 0.007677700 1 0.007677700 <NA>
## 17 heartRate.nonNA 0.006531896 0 0.006531896 <NA>
## 1 .rnorm -0.019000943 0 0.019000943 <NA>
## 13 education.nonNA -0.057873618 0 0.057873618 <NA>
## 12 education -0.058939008 1 0.058939008 <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 22 5.577566 0.07256894 FALSE FALSE FALSE FALSE
## 2 1.025424 1.41509434 FALSE FALSE FALSE FALSE
## 21 1.014493 7.94629898 FALSE FALSE FALSE FALSE
## 19 2.197216 0.07256894 FALSE FALSE FALSE FALSE
## 11 1.702128 4.89840348 FALSE FALSE FALSE FALSE
## 14 1.072072 4.24528302 FALSE FALSE FALSE FALSE
## 15 1.145299 4.28156749 FALSE FALSE FALSE FALSE
## 24 1.204545 8.38171263 FALSE FALSE FALSE FALSE
## 23 1.181818 8.38171263 FALSE FALSE FALSE FALSE
## 5 34.337662 0.07256894 FALSE TRUE FALSE FALSE
## 6 34.333333 0.07256894 FALSE TRUE FALSE FALSE
## 18 1.365665 0.07256894 FALSE FALSE FALSE FALSE
## 3 1.142857 40.89259797 FALSE FALSE FALSE FALSE
## 4 1.142857 40.89259797 FALSE FALSE FALSE FALSE
## 20 152.111111 0.07256894 FALSE TRUE FALSE FALSE
## 10 37.816901 0.07256894 FALSE TRUE FALSE FALSE
## 7 2.991507 1.16110305 FALSE FALSE FALSE FALSE
## 8 2.966316 1.16110305 FALSE FALSE FALSE FALSE
## 9 1.046028 0.07256894 FALSE FALSE FALSE TRUE
## 16 1.485944 2.46734398 FALSE FALSE FALSE TRUE
## 17 1.485944 2.46734398 FALSE FALSE FALSE TRUE
## 1 1.000000 100.00000000 FALSE FALSE FALSE FALSE
## 13 1.392298 0.14513788 FALSE FALSE FALSE FALSE
## 12 1.375765 0.14513788 FALSE FALSE FALSE FALSE#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_point).
print(subset(glb_feats_df, myNearZV))## [1] id cor.y exclude.as.feat cor.y.abs
## [5] cor.high.X freqRatio percentUnique zeroVar
## [9] nzv myNearZV is.cor.y.abs.low
## <0 rows> (or 0-length row.names)glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## education cigsPerDay BPMeds totChol BMI heartRate
## 105 29 53 50 19 1
## glucose
## 388
## [1] "numeric data w/ 0s in : "
## male currentSmoker cigsPerDay BPMeds
## 2420 2145 2145 4063
## prevalentStroke prevalentHyp diabetes TenYearCHD
## 4215 2923 4131 3596
## cigsPerDay.nonNA BPMeds.nonNA
## 2145 4113
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## .rownames
## 0# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 30.357 31.135 0.778
## 9 partition.data.training 6 0 31.135 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=1 - (nrow(glb_newobs_df) * 1.1 / nrow(glb_trnobs_df)))
glb_fitobs_df <- glb_trnobs_df[split, ]
glb_OOBobs_df <- glb_trnobs_df[!split ,]
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## [1] "Newdata contains non-NA data for TenYearCHD.fctr; setting OOB to Newdata"if (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_vars)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_vars)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_vars)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_vars
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}
# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 24 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## TenYearCHD.fctr TenYearCHD.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 22 TenYearCHD 1 TRUE 1 <NA>
## TenYearCHD.fctr TenYearCHD.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV
## 22 5.577566 0.07256894 FALSE FALSE FALSE
## TenYearCHD.fctr NA NA NA NA NA
## is.cor.y.abs.low interaction.feat rsp_var_raw rsp_var
## 22 FALSE NA TRUE NA
## TenYearCHD.fctr NA NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## character(0)print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 4240 28print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 2756 27print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 2756 27print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 1484 27print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 1484 27# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 31.135 31.473 0.338
## 10 fit.models 7 0 31.473 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a lower correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl_fn(model_id="Baseline", model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.8479681 0.1520319
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8479681 0.1520319
## 2 0.8479681 0.1520319
## 3 0.8479681 0.1520319
## 4 0.8479681 0.1520319
## 5 0.8479681 0.1520319
## 6 0.8479681 0.1520319
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.MFO.myMFO_classfr.N
## 1 N 2337
## 2 Y 419
## Prediction
## Reference N Y
## N 2337 0
## Y 419 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.479681e-01 0.000000e+00 8.340130e-01 8.611796e-01 8.479681e-01
## AccuracyPValue McnemarPValue
## 5.130333e-01 1.096271e-92
## [1] " calling mypredict_mdl for OOB:"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8479681 0.1520319
## 2 0.8479681 0.1520319
## 3 0.8479681 0.1520319
## 4 0.8479681 0.1520319
## 5 0.8479681 0.1520319
## 6 0.8479681 0.1520319
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## TenYearCHD.fctr TenYearCHD.fctr.predict.MFO.myMFO_classfr.N
## 1 N 1259
## 2 Y 225
## Prediction
## Reference N Y
## N 1259 0
## Y 225 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.483827e-01 0.000000e+00 8.291112e-01 8.662638e-01 8.483827e-01
## AccuracyPValue McnemarPValue
## 5.177806e-01 2.000126e-50
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.267 0.003 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8479681
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.834013 0.8611796 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8483827
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8291112 0.8662638 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.2639370
## 2 0.1 0.2639370
## 3 0.2 0.1454994
## 4 0.3 0.1454994
## 5 0.4 0.1454994
## 6 0.5 0.1454994
## 7 0.6 0.1454994
## 8 0.7 0.1454994
## 9 0.8 0.1454994
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Random.myrandom_classfr.Y
## 1 N 2337
## 2 Y 419
## Prediction
## Reference N Y
## N 0 2337
## Y 0 419
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.1520319 0.0000000 0.1388204 0.1659870 0.8479681
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] " calling mypredict_mdl for OOB:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.2633119
## 2 0.1 0.2633119
## 3 0.2 0.1755196
## 4 0.3 0.1755196
## 5 0.4 0.1755196
## 6 0.5 0.1755196
## 7 0.6 0.1755196
## 8 0.7 0.1755196
## 9 0.8 0.1755196
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.OOB"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Random.myrandom_classfr.Y
## 1 N 1259
## 2 Y 225
## Prediction
## Reference N Y
## N 0 1259
## Y 0 225
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.516173e-01 0.000000e+00 1.337362e-01 1.708888e-01 8.483827e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 2.498464e-275
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.231 0.002 0.4991605
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.1 0.263937 0.1520319
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.1388204 0.165987 0 0.5169305
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.2633119 0.1516173
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.1337362 0.1708888 0# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: age, sysBP"## Loading required package: rpart## Fitting cp = 0.00398 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 2756
##
## CP nsplit rel error
## 1 0.003977725 0 1
##
## Node number 1: 2756 observations
## predicted class=N expected loss=0.1520319 P(node) =1
## class counts: 2337 419
## probabilities: 0.848 0.152
##
## n= 2756
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 2756 419 N (0.8479681 0.1520319) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 2337
## 2 Y 419
## Prediction
## Reference N Y
## N 2337 0
## Y 419 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.479681e-01 0.000000e+00 8.340130e-01 8.611796e-01 8.479681e-01
## AccuracyPValue McnemarPValue
## 5.130333e-01 1.096271e-92
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 1259
## 2 Y 225
## Prediction
## Reference N Y
## N 1259 0
## Y 225 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.483827e-01 0.000000e+00 8.291112e-01 8.662638e-01 8.483827e-01
## AccuracyPValue McnemarPValue
## 5.177806e-01 2.000126e-50
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.rpart rpart age, sysBP 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.586 0.046 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8479681
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.834013 0.8611796 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8483827
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8291112 0.8662638 0ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: age, sysBP"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 2756
##
## CP nsplit rel error
## 1 0.0039777247 0 1.0000000
## 2 0.0023866348 3 0.9880668
## 3 0.0017899761 7 0.9785203
## 4 0.0006818957 20 0.9451074
## 5 0.0000000000 27 0.9403341
##
## Variable importance
## sysBP age
## 54 46
##
## Node number 1: 2756 observations, complexity param=0.003977725
## predicted class=N expected loss=0.1520319 P(node) =1
## class counts: 2337 419
## probabilities: 0.848 0.152
## left son=2 (1511 obs) right son=3 (1245 obs)
## Primary splits:
## age < 50.5 to the left, improve=27.41007, (0 missing)
## sysBP < 144.25 to the left, improve=23.70079, (0 missing)
## Surrogate splits:
## sysBP < 137.75 to the left, agree=0.667, adj=0.263, (0 split)
##
## Node number 2: 1511 observations
## predicted class=N expected loss=0.08802118 P(node) =0.5482583
## class counts: 1378 133
## probabilities: 0.912 0.088
##
## Node number 3: 1245 observations, complexity param=0.003977725
## predicted class=N expected loss=0.2297189 P(node) =0.4517417
## class counts: 959 286
## probabilities: 0.770 0.230
## left son=6 (767 obs) right son=7 (478 obs)
## Primary splits:
## sysBP < 144.25 to the left, improve=9.907748, (0 missing)
## age < 64.5 to the left, improve=5.245415, (0 missing)
## Surrogate splits:
## age < 62.5 to the left, agree=0.635, adj=0.05, (0 split)
##
## Node number 6: 767 observations, complexity param=0.0006818957
## predicted class=N expected loss=0.1799218 P(node) =0.2783019
## class counts: 629 138
## probabilities: 0.820 0.180
## left son=12 (734 obs) right son=13 (33 obs)
## Primary splits:
## age < 65.5 to the left, improve=2.327719, (0 missing)
## sysBP < 113.25 to the left, improve=1.352177, (0 missing)
##
## Node number 7: 478 observations, complexity param=0.003977725
## predicted class=N expected loss=0.3096234 P(node) =0.1734398
## class counts: 330 148
## probabilities: 0.690 0.310
## left son=14 (461 obs) right son=15 (17 obs)
## Primary splits:
## sysBP < 209 to the left, improve=4.014090, (0 missing)
## age < 54.5 to the left, improve=3.039295, (0 missing)
##
## Node number 12: 734 observations, complexity param=0.0006818957
## predicted class=N expected loss=0.1716621 P(node) =0.266328
## class counts: 608 126
## probabilities: 0.828 0.172
## left son=24 (123 obs) right son=25 (611 obs)
## Primary splits:
## sysBP < 113.25 to the left, improve=1.6227060, (0 missing)
## age < 59.5 to the left, improve=0.5307999, (0 missing)
##
## Node number 13: 33 observations
## predicted class=N expected loss=0.3636364 P(node) =0.01197388
## class counts: 21 12
## probabilities: 0.636 0.364
##
## Node number 14: 461 observations, complexity param=0.002386635
## predicted class=N expected loss=0.29718 P(node) =0.1672714
## class counts: 324 137
## probabilities: 0.703 0.297
## left son=28 (98 obs) right son=29 (363 obs)
## Primary splits:
## age < 54.5 to the left, improve=2.1574210, (0 missing)
## sysBP < 174.25 to the left, improve=0.9362037, (0 missing)
##
## Node number 15: 17 observations
## predicted class=Y expected loss=0.3529412 P(node) =0.00616836
## class counts: 6 11
## probabilities: 0.353 0.647
##
## Node number 24: 123 observations
## predicted class=N expected loss=0.09756098 P(node) =0.0446299
## class counts: 111 12
## probabilities: 0.902 0.098
##
## Node number 25: 611 observations, complexity param=0.0006818957
## predicted class=N expected loss=0.1865794 P(node) =0.2216981
## class counts: 497 114
## probabilities: 0.813 0.187
## left son=50 (75 obs) right son=51 (536 obs)
## Primary splits:
## sysBP < 140.25 to the right, improve=0.7579615, (0 missing)
## age < 57.5 to the left, improve=0.5568661, (0 missing)
##
## Node number 28: 98 observations
## predicted class=N expected loss=0.2040816 P(node) =0.03555878
## class counts: 78 20
## probabilities: 0.796 0.204
##
## Node number 29: 363 observations, complexity param=0.002386635
## predicted class=N expected loss=0.322314 P(node) =0.1317126
## class counts: 246 117
## probabilities: 0.678 0.322
## left son=58 (309 obs) right son=59 (54 obs)
## Primary splits:
## age < 64.5 to the left, improve=0.5623312, (0 missing)
## sysBP < 149.75 to the left, improve=0.5553818, (0 missing)
##
## Node number 50: 75 observations
## predicted class=N expected loss=0.12 P(node) =0.02721335
## class counts: 66 9
## probabilities: 0.880 0.120
##
## Node number 51: 536 observations, complexity param=0.0006818957
## predicted class=N expected loss=0.1958955 P(node) =0.1944848
## class counts: 431 105
## probabilities: 0.804 0.196
## left son=102 (483 obs) right son=103 (53 obs)
## Primary splits:
## sysBP < 137.75 to the left, improve=1.3214890, (0 missing)
## age < 57.5 to the left, improve=0.8333108, (0 missing)
##
## Node number 58: 309 observations, complexity param=0.001789976
## predicted class=N expected loss=0.3106796 P(node) =0.112119
## class counts: 213 96
## probabilities: 0.689 0.311
## left son=116 (8 obs) right son=117 (301 obs)
## Primary splits:
## sysBP < 198.5 to the right, improve=1.5853950, (0 missing)
## age < 56.5 to the right, improve=0.3752841, (0 missing)
##
## Node number 59: 54 observations, complexity param=0.002386635
## predicted class=N expected loss=0.3888889 P(node) =0.01959361
## class counts: 33 21
## probabilities: 0.611 0.389
## left son=118 (40 obs) right son=119 (14 obs)
## Primary splits:
## sysBP < 152.25 to the right, improve=1.2595240, (0 missing)
## age < 66.5 to the right, improve=0.1666667, (0 missing)
##
## Node number 102: 483 observations
## predicted class=N expected loss=0.184265 P(node) =0.175254
## class counts: 394 89
## probabilities: 0.816 0.184
##
## Node number 103: 53 observations, complexity param=0.0006818957
## predicted class=N expected loss=0.3018868 P(node) =0.01923077
## class counts: 37 16
## probabilities: 0.698 0.302
## left son=206 (16 obs) right son=207 (37 obs)
## Primary splits:
## sysBP < 139.5 to the right, improve=0.5997578, (0 missing)
## age < 54.5 to the left, improve=0.1234064, (0 missing)
##
## Node number 116: 8 observations
## predicted class=N expected loss=0 P(node) =0.002902758
## class counts: 8 0
## probabilities: 1.000 0.000
##
## Node number 117: 301 observations, complexity param=0.001789976
## predicted class=N expected loss=0.3189369 P(node) =0.1092163
## class counts: 205 96
## probabilities: 0.681 0.319
## left son=234 (288 obs) right son=235 (13 obs)
## Primary splits:
## sysBP < 191.75 to the left, improve=1.3095260, (0 missing)
## age < 56.5 to the right, improve=0.3481652, (0 missing)
##
## Node number 118: 40 observations, complexity param=0.002386635
## predicted class=N expected loss=0.325 P(node) =0.01451379
## class counts: 27 13
## probabilities: 0.675 0.325
## left son=236 (30 obs) right son=237 (10 obs)
## Primary splits:
## sysBP < 182.25 to the left, improve=2.01666700, (0 missing)
## age < 65.5 to the left, improve=0.06648352, (0 missing)
##
## Node number 119: 14 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.005079826
## class counts: 6 8
## probabilities: 0.429 0.571
##
## Node number 206: 16 observations
## predicted class=N expected loss=0.1875 P(node) =0.005805515
## class counts: 13 3
## probabilities: 0.812 0.188
##
## Node number 207: 37 observations, complexity param=0.0006818957
## predicted class=N expected loss=0.3513514 P(node) =0.01342525
## class counts: 24 13
## probabilities: 0.649 0.351
## left son=414 (12 obs) right son=415 (25 obs)
## Primary splits:
## age < 58.5 to the right, improve=1.21153200, (0 missing)
## sysBP < 138.25 to the right, improve=0.04435204, (0 missing)
##
## Node number 234: 288 observations, complexity param=0.001789976
## predicted class=N expected loss=0.3090278 P(node) =0.1044993
## class counts: 199 89
## probabilities: 0.691 0.309
## left son=468 (26 obs) right son=469 (262 obs)
## Primary splits:
## sysBP < 182.25 to the right, improve=1.3764970, (0 missing)
## age < 56.5 to the right, improve=0.4030947, (0 missing)
##
## Node number 235: 13 observations
## predicted class=Y expected loss=0.4615385 P(node) =0.004716981
## class counts: 6 7
## probabilities: 0.462 0.538
##
## Node number 236: 30 observations
## predicted class=N expected loss=0.2333333 P(node) =0.01088534
## class counts: 23 7
## probabilities: 0.767 0.233
##
## Node number 237: 10 observations
## predicted class=Y expected loss=0.4 P(node) =0.003628447
## class counts: 4 6
## probabilities: 0.400 0.600
##
## Node number 414: 12 observations
## predicted class=N expected loss=0.1666667 P(node) =0.004354136
## class counts: 10 2
## probabilities: 0.833 0.167
##
## Node number 415: 25 observations, complexity param=0.0006818957
## predicted class=N expected loss=0.44 P(node) =0.009071118
## class counts: 14 11
## probabilities: 0.560 0.440
## left son=830 (13 obs) right son=831 (12 obs)
## Primary splits:
## age < 54.5 to the left, improve=0.94820510, (0 missing)
## sysBP < 138.75 to the left, improve=0.05333333, (0 missing)
## Surrogate splits:
## sysBP < 138.75 to the right, agree=0.6, adj=0.167, (0 split)
##
## Node number 468: 26 observations
## predicted class=N expected loss=0.1538462 P(node) =0.009433962
## class counts: 22 4
## probabilities: 0.846 0.154
##
## Node number 469: 262 observations, complexity param=0.001789976
## predicted class=N expected loss=0.3244275 P(node) =0.09506531
## class counts: 177 85
## probabilities: 0.676 0.324
## left son=938 (248 obs) right son=939 (14 obs)
## Primary splits:
## sysBP < 179.5 to the left, improve=1.8047020, (0 missing)
## age < 63.5 to the right, improve=0.3939949, (0 missing)
##
## Node number 830: 13 observations
## predicted class=N expected loss=0.3076923 P(node) =0.004716981
## class counts: 9 4
## probabilities: 0.692 0.308
##
## Node number 831: 12 observations
## predicted class=Y expected loss=0.4166667 P(node) =0.004354136
## class counts: 5 7
## probabilities: 0.417 0.583
##
## Node number 938: 248 observations, complexity param=0.001789976
## predicted class=N expected loss=0.3104839 P(node) =0.08998549
## class counts: 171 77
## probabilities: 0.690 0.310
## left son=1876 (15 obs) right son=1877 (233 obs)
## Primary splits:
## sysBP < 145.25 to the left, improve=1.0020790, (0 missing)
## age < 63.5 to the right, improve=0.2762462, (0 missing)
##
## Node number 939: 14 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.005079826
## class counts: 6 8
## probabilities: 0.429 0.571
##
## Node number 1876: 15 observations
## predicted class=N expected loss=0.1333333 P(node) =0.005442671
## class counts: 13 2
## probabilities: 0.867 0.133
##
## Node number 1877: 233 observations, complexity param=0.001789976
## predicted class=N expected loss=0.3218884 P(node) =0.08454282
## class counts: 158 75
## probabilities: 0.678 0.322
## left son=3754 (219 obs) right son=3755 (14 obs)
## Primary splits:
## sysBP < 146.25 to the right, improve=1.8550290, (0 missing)
## age < 63.5 to the right, improve=0.5573179, (0 missing)
##
## Node number 3754: 219 observations, complexity param=0.001789976
## predicted class=N expected loss=0.3059361 P(node) =0.07946299
## class counts: 152 67
## probabilities: 0.694 0.306
## left son=7508 (30 obs) right son=7509 (189 obs)
## Primary splits:
## sysBP < 149.75 to the left, improve=2.0712330, (0 missing)
## age < 56.5 to the right, improve=0.7170902, (0 missing)
##
## Node number 3755: 14 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.005079826
## class counts: 6 8
## probabilities: 0.429 0.571
##
## Node number 7508: 30 observations
## predicted class=N expected loss=0.1333333 P(node) =0.01088534
## class counts: 26 4
## probabilities: 0.867 0.133
##
## Node number 7509: 189 observations, complexity param=0.001789976
## predicted class=N expected loss=0.3333333 P(node) =0.06857765
## class counts: 126 63
## probabilities: 0.667 0.333
## left son=15018 (45 obs) right son=15019 (144 obs)
## Primary splits:
## age < 62.5 to the right, improve=0.525, (0 missing)
## sysBP < 157.5 to the right, improve=0.336, (0 missing)
##
## Node number 15018: 45 observations
## predicted class=N expected loss=0.2666667 P(node) =0.01632801
## class counts: 33 12
## probabilities: 0.733 0.267
##
## Node number 15019: 144 observations, complexity param=0.001789976
## predicted class=N expected loss=0.3541667 P(node) =0.05224964
## class counts: 93 51
## probabilities: 0.646 0.354
## left son=30038 (94 obs) right son=30039 (50 obs)
## Primary splits:
## sysBP < 157.5 to the right, improve=0.66393620, (0 missing)
## age < 55.5 to the right, improve=0.09758065, (0 missing)
##
## Node number 30038: 94 observations, complexity param=0.001789976
## predicted class=N expected loss=0.3191489 P(node) =0.0341074
## class counts: 64 30
## probabilities: 0.681 0.319
## left son=60076 (69 obs) right son=60077 (25 obs)
## Primary splits:
## age < 56.5 to the right, improve=0.9948319, (0 missing)
## sysBP < 174.5 to the left, improve=0.2333423, (0 missing)
##
## Node number 30039: 50 observations, complexity param=0.001789976
## predicted class=N expected loss=0.42 P(node) =0.01814224
## class counts: 29 21
## probabilities: 0.580 0.420
## left son=60078 (41 obs) right son=60079 (9 obs)
## Primary splits:
## sysBP < 155.25 to the left, improve=2.8098640, (0 missing)
## age < 56.5 to the left, improve=0.9126316, (0 missing)
##
## Node number 60076: 69 observations
## predicted class=N expected loss=0.2753623 P(node) =0.02503628
## class counts: 50 19
## probabilities: 0.725 0.275
##
## Node number 60077: 25 observations, complexity param=0.001789976
## predicted class=N expected loss=0.44 P(node) =0.009071118
## class counts: 14 11
## probabilities: 0.560 0.440
## left son=120154 (18 obs) right son=120155 (7 obs)
## Primary splits:
## sysBP < 161.25 to the right, improve=1.4628570, (0 missing)
## age < 55.5 to the left, improve=0.4368831, (0 missing)
##
## Node number 60078: 41 observations, complexity param=0.001789976
## predicted class=N expected loss=0.3414634 P(node) =0.01487663
## class counts: 27 14
## probabilities: 0.659 0.341
## left son=120156 (30 obs) right son=120157 (11 obs)
## Primary splits:
## age < 60.5 to the left, improve=1.2511460, (0 missing)
## sysBP < 150.25 to the right, improve=0.4996305, (0 missing)
##
## Node number 60079: 9 observations
## predicted class=Y expected loss=0.2222222 P(node) =0.003265602
## class counts: 2 7
## probabilities: 0.222 0.778
##
## Node number 120154: 18 observations
## predicted class=N expected loss=0.3333333 P(node) =0.006531205
## class counts: 12 6
## probabilities: 0.667 0.333
##
## Node number 120155: 7 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.002539913
## class counts: 2 5
## probabilities: 0.286 0.714
##
## Node number 120156: 30 observations
## predicted class=N expected loss=0.2666667 P(node) =0.01088534
## class counts: 22 8
## probabilities: 0.733 0.267
##
## Node number 120157: 11 observations
## predicted class=Y expected loss=0.4545455 P(node) =0.003991292
## class counts: 5 6
## probabilities: 0.455 0.545
##
## n= 2756
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 2756 419 N (0.84796807 0.15203193)
## 2) age< 50.5 1511 133 N (0.91197882 0.08802118) *
## 3) age>=50.5 1245 286 N (0.77028112 0.22971888)
## 6) sysBP< 144.25 767 138 N (0.82007823 0.17992177)
## 12) age< 65.5 734 126 N (0.82833787 0.17166213)
## 24) sysBP< 113.25 123 12 N (0.90243902 0.09756098) *
## 25) sysBP>=113.25 611 114 N (0.81342062 0.18657938)
## 50) sysBP>=140.25 75 9 N (0.88000000 0.12000000) *
## 51) sysBP< 140.25 536 105 N (0.80410448 0.19589552)
## 102) sysBP< 137.75 483 89 N (0.81573499 0.18426501) *
## 103) sysBP>=137.75 53 16 N (0.69811321 0.30188679)
## 206) sysBP>=139.5 16 3 N (0.81250000 0.18750000) *
## 207) sysBP< 139.5 37 13 N (0.64864865 0.35135135)
## 414) age>=58.5 12 2 N (0.83333333 0.16666667) *
## 415) age< 58.5 25 11 N (0.56000000 0.44000000)
## 830) age< 54.5 13 4 N (0.69230769 0.30769231) *
## 831) age>=54.5 12 5 Y (0.41666667 0.58333333) *
## 13) age>=65.5 33 12 N (0.63636364 0.36363636) *
## 7) sysBP>=144.25 478 148 N (0.69037657 0.30962343)
## 14) sysBP< 209 461 137 N (0.70281996 0.29718004)
## 28) age< 54.5 98 20 N (0.79591837 0.20408163) *
## 29) age>=54.5 363 117 N (0.67768595 0.32231405)
## 58) age< 64.5 309 96 N (0.68932039 0.31067961)
## 116) sysBP>=198.5 8 0 N (1.00000000 0.00000000) *
## 117) sysBP< 198.5 301 96 N (0.68106312 0.31893688)
## 234) sysBP< 191.75 288 89 N (0.69097222 0.30902778)
## 468) sysBP>=182.25 26 4 N (0.84615385 0.15384615) *
## 469) sysBP< 182.25 262 85 N (0.67557252 0.32442748)
## 938) sysBP< 179.5 248 77 N (0.68951613 0.31048387)
## 1876) sysBP< 145.25 15 2 N (0.86666667 0.13333333) *
## 1877) sysBP>=145.25 233 75 N (0.67811159 0.32188841)
## 3754) sysBP>=146.25 219 67 N (0.69406393 0.30593607)
## 7508) sysBP< 149.75 30 4 N (0.86666667 0.13333333) *
## 7509) sysBP>=149.75 189 63 N (0.66666667 0.33333333)
## 15018) age>=62.5 45 12 N (0.73333333 0.26666667) *
## 15019) age< 62.5 144 51 N (0.64583333 0.35416667)
## 30038) sysBP>=157.5 94 30 N (0.68085106 0.31914894)
## 60076) age>=56.5 69 19 N (0.72463768 0.27536232) *
## 60077) age< 56.5 25 11 N (0.56000000 0.44000000)
## 120154) sysBP>=161.25 18 6 N (0.66666667 0.33333333) *
## 120155) sysBP< 161.25 7 2 Y (0.28571429 0.71428571) *
## 30039) sysBP< 157.5 50 21 N (0.58000000 0.42000000)
## 60078) sysBP< 155.25 41 14 N (0.65853659 0.34146341)
## 120156) age< 60.5 30 8 N (0.73333333 0.26666667) *
## 120157) age>=60.5 11 5 Y (0.45454545 0.54545455) *
## 60079) sysBP>=155.25 9 2 Y (0.22222222 0.77777778) *
## 3755) sysBP< 146.25 14 6 Y (0.42857143 0.57142857) *
## 939) sysBP>=179.5 14 6 Y (0.42857143 0.57142857) *
## 235) sysBP>=191.75 13 6 Y (0.46153846 0.53846154) *
## 59) age>=64.5 54 21 N (0.61111111 0.38888889)
## 118) sysBP>=152.25 40 13 N (0.67500000 0.32500000)
## 236) sysBP< 182.25 30 7 N (0.76666667 0.23333333) *
## 237) sysBP>=182.25 10 4 Y (0.40000000 0.60000000) *
## 119) sysBP< 152.25 14 6 Y (0.42857143 0.57142857) *
## 15) sysBP>=209 17 6 Y (0.35294118 0.64705882) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.26393701
## 2 0.1 0.35746902
## 3 0.2 0.36757991
## 4 0.3 0.31456954
## 5 0.4 0.27037037
## 6 0.5 0.27037037
## 7 0.6 0.10176991
## 8 0.7 0.05517241
## 9 0.8 0.00000000
## 10 0.9 0.00000000
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 2041
## 2 Y 258
## TenYearCHD.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 296
## 2 161
## Prediction
## Reference N Y
## N 2041 296
## Y 258 161
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.7989840 0.2483476 0.7835265 0.8138039 0.8479681
## AccuracyPValue McnemarPValue
## 1.0000000 0.1159553
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.263311878
## 2 0.1 0.332065906
## 3 0.2 0.285714286
## 4 0.3 0.153846154
## 5 0.4 0.097378277
## 6 0.5 0.097378277
## 7 0.6 0.025210084
## 8 0.7 0.008547009
## 9 0.8 0.000000000
## 10 0.9 0.000000000
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.OOB"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 826
## 2 Y 94
## TenYearCHD.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 433
## 2 131
## Prediction
## Reference N Y
## N 826 433
## Y 94 131
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.448787e-01 1.472157e-01 6.199261e-01 6.692564e-01 8.483827e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 4.553881e-49
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart age, sysBP 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.487 0.043 0.6874657
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.2 0.3675799 0.798984
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7835265 0.8138039 0.2483476 0.6261266
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.3320659 0.6448787
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.6199261 0.6692564 0.1472157if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: age, sysBP"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00398 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 2756
##
## CP nsplit rel error
## 1 0.003977725 0 1
##
## Node number 1: 2756 observations
## predicted class=N expected loss=0.1520319 P(node) =1
## class counts: 2337 419
## probabilities: 0.848 0.152
##
## n= 2756
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 2756 419 N (0.8479681 0.1520319) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Max.cor.Y.rpart.N
## 1 N 2337
## 2 Y 419
## Prediction
## Reference N Y
## N 2337 0
## Y 419 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.479681e-01 0.000000e+00 8.340130e-01 8.611796e-01 8.479681e-01
## AccuracyPValue McnemarPValue
## 5.130333e-01 1.096271e-92
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Max.cor.Y.rpart.N
## 1 N 1259
## 2 Y 225
## Prediction
## Reference N Y
## N 1259 0
## Y 225 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.483827e-01 0.000000e+00 8.291112e-01 8.662638e-01 8.483827e-01
## AccuracyPValue McnemarPValue
## 5.177806e-01 2.000126e-50
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart age, sysBP 3
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.294 0.061 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8436122
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.834013 0.8611796 0.01359321 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8483827
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8291112 0.8662638 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.00449187 0.01395845# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: age, sysBP"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3273 -0.6051 -0.4543 -0.3519 2.6420
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.734453 0.404734 -16.639 < 2e-16 ***
## age 0.059106 0.007118 8.304 < 2e-16 ***
## sysBP 0.014450 0.002383 6.063 1.34e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2349.3 on 2755 degrees of freedom
## Residual deviance: 2170.9 on 2753 degrees of freedom
## AIC: 2176.9
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.263937008
## 2 0.1 0.331125828
## 3 0.2 0.361467890
## 4 0.3 0.252252252
## 5 0.4 0.107438017
## 6 0.5 0.041666667
## 7 0.6 0.014218009
## 8 0.7 0.004761905
## 9 0.8 0.000000000
## 10 0.9 0.000000000
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Max.cor.Y.glm.N
## 1 N 1863
## 2 Y 222
## TenYearCHD.fctr.predict.Max.cor.Y.glm.Y
## 1 474
## 2 197
## Prediction
## Reference N Y
## N 1863 474
## Y 222 197
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.474601e-01 2.144231e-01 7.308005e-01 7.635925e-01 8.479681e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.832389e-21
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.26331188
## 2 0.1 0.33715799
## 3 0.2 0.36209814
## 4 0.3 0.26865672
## 5 0.4 0.08870968
## 6 0.5 0.02608696
## 7 0.6 0.00000000
## 8 0.7 0.00000000
## 9 0.8 0.00000000
## 10 0.9 0.00000000
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Max.cor.Y.glm.N
## 1 N 1000
## 2 Y 118
## TenYearCHD.fctr.predict.Max.cor.Y.glm.Y
## 1 259
## 2 107
## Prediction
## Reference N Y
## N 1000 259
## Y 118 107
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.459569e-01 2.146098e-01 7.229984e-01 7.679377e-01 8.483827e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 5.580134e-13
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.glm glm age, sysBP 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.971 0.051 0.6995291
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.2 0.3614679 0.849783
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7308005 0.7635925 0.03285851 0.7108252
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.3620981 0.7459569
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7229984 0.7679377 0.2146098 2176.886
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.002818894 0.01435593if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.glm"
## [1] " indep_vars: age, sysBP, age:sysBP"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2120 -0.6163 -0.4536 -0.3378 2.7634
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -9.2140504 2.1572857 -4.271 1.94e-05 ***
## age 0.1039283 0.0389277 2.670 0.00759 **
## sysBP 0.0328995 0.0158792 2.072 0.03828 *
## `age:sysBP` -0.0003307 0.0002815 -1.175 0.24013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2349.3 on 2755 degrees of freedom
## Residual deviance: 2169.5 on 2752 degrees of freedom
## AIC: 2177.5
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.263937008
## 2 0.1 0.331758034
## 3 0.2 0.359019264
## 4 0.3 0.259701493
## 5 0.4 0.101694915
## 6 0.5 0.023474178
## 7 0.6 0.004761905
## 8 0.7 0.004761905
## 9 0.8 0.000000000
## 10 0.9 0.000000000
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 1819
## 2 Y 214
## TenYearCHD.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 518
## 2 205
## Prediction
## Reference N Y
## N 1819 518
## Y 214 205
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.343977e-01 2.062131e-01 7.174842e-01 7.508120e-01 8.479681e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 4.114215e-29
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.263311878
## 2 0.1 0.338893766
## 3 0.2 0.366242038
## 4 0.3 0.261127596
## 5 0.4 0.066666667
## 6 0.5 0.008810573
## 7 0.6 0.000000000
## 8 0.7 0.000000000
## 9 0.8 0.000000000
## 10 0.9 0.000000000
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 971
## 2 Y 110
## TenYearCHD.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 288
## 2 115
## Prediction
## Reference N Y
## N 971 288
## Y 110 115
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.318059e-01 2.131223e-01 7.084848e-01 7.542060e-01 8.483827e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 7.170948e-19
## model_id model_method feats
## 1 Interact.High.cor.Y.glm glm age, sysBP, age:sysBP
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.021 0.058
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.69957 0.2 0.3590193 0.8486941
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7174842 0.750812 0.01782193 0.7114853
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.366242 0.7318059
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7084848 0.754206 0.2131223 2177.511
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.001007458 0.006917152# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":", feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: age, sysBP, prevalentHyp, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, .rnorm, education.nonNA"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8416 -0.6040 -0.4335 -0.2909 2.7945
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.043981 0.793193 -10.141 < 2e-16 ***
## age 0.062270 0.007617 8.175 2.96e-16 ***
## sysBP 0.009767 0.003341 2.924 0.00346 **
## prevalentHyp 0.289330 0.157275 1.840 0.06582 .
## glucose.nonNA 0.007811 0.002659 2.938 0.00330 **
## totChol.nonNA 0.003211 0.001276 2.517 0.01182 *
## BPMeds.nonNA 0.342024 0.273340 1.251 0.21083
## male 0.502425 0.124127 4.048 5.17e-05 ***
## BMI.nonNA 0.007127 0.014500 0.491 0.62309
## prevalentStroke 1.360637 0.519648 2.618 0.00883 **
## diabetes -0.164752 0.369521 -0.446 0.65570
## cigsPerDay.nonNA 0.016615 0.007163 2.320 0.02037 *
## currentSmoker 0.073394 0.178392 0.411 0.68077
## heartRate.nonNA -0.004818 0.004858 -0.992 0.32136
## .rnorm -0.066397 0.057142 -1.162 0.24525
## education.nonNA -0.017798 0.057579 -0.309 0.75724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2349.3 on 2755 degrees of freedom
## Residual deviance: 2092.4 on 2740 degrees of freedom
## AIC: 2124.4
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.263937008
## 2 0.1 0.346718903
## 3 0.2 0.387267905
## 4 0.3 0.321479374
## 5 0.4 0.183206107
## 6 0.5 0.126361656
## 7 0.6 0.059770115
## 8 0.7 0.032786885
## 9 0.8 0.018867925
## 10 0.9 0.009501188
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Low.cor.X.glm.N
## 1 N 1844
## 2 Y 200
## TenYearCHD.fctr.predict.Low.cor.X.glm.Y
## 1 493
## 2 219
## Prediction
## Reference N Y
## N 1844 493
## Y 200 219
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.485486e-01 2.422143e-01 7.319111e-01 7.646567e-01 8.479681e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.369364e-28
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.26331188
## 2 0.1 0.35078053
## 3 0.2 0.39065109
## 4 0.3 0.29551451
## 5 0.4 0.20494700
## 6 0.5 0.08230453
## 7 0.6 0.04273504
## 8 0.7 0.01754386
## 9 0.8 0.00000000
## 10 0.9 0.00000000
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Low.cor.X.glm.N
## 1 N 1002
## 2 Y 108
## TenYearCHD.fctr.predict.Low.cor.X.glm.Y
## 1 257
## 2 117
## Prediction
## Reference N Y
## N 1002 257
## Y 108 117
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.540431e-01 2.483375e-01 7.313056e-01 7.757707e-01 8.483827e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 9.433068e-15
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 age, sysBP, prevalentHyp, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, .rnorm, education.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.451 0.145
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7295872 0.2 0.3872679 0.8519609
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7319111 0.7646567 0.09050139 0.7357374
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.3906511 0.7540431
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7313056 0.7757707 0.2483375 2124.378
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004918549 0.04963018rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 31.473 59.43 27.958
## 11 fit.models 7 1 59.431 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 63.707 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
# if (glb_is_classification && glb_is_binomial) {
# model_id_pfx <- "Conditional.X"
# # indep_vars_vctr <- setdiff(names(glb_fitobs_df), union(glb_rsp_var, glb_exclude_vars_as_features))
# indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"]
# } else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
# }
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 63.707 63.726 0.019
## 2 fit.models_1_glm 2 0 63.726 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, .rnorm, education.nonNA"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8843 -0.6025 -0.4325 -0.2922 2.7774
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.858459 0.824711 -9.529 < 2e-16 ***
## age 0.061021 0.007762 7.862 3.79e-15 ***
## sysBP 0.011901 0.004256 2.796 0.00517 **
## prevalentHyp 0.309309 0.159398 1.940 0.05232 .
## diaBP -0.005972 0.007381 -0.809 0.41848
## glucose.nonNA 0.007745 0.002663 2.908 0.00364 **
## totChol.nonNA 0.003218 0.001276 2.523 0.01164 *
## BPMeds.nonNA 0.338660 0.273898 1.236 0.21629
## male 0.514269 0.125041 4.113 3.91e-05 ***
## BMI.nonNA 0.009514 0.014815 0.642 0.52078
## prevalentStroke 1.374075 0.520203 2.641 0.00826 **
## diabetes -0.180211 0.370054 -0.487 0.62627
## cigsPerDay.nonNA 0.016587 0.007159 2.317 0.02050 *
## currentSmoker 0.072429 0.178428 0.406 0.68480
## heartRate.nonNA -0.004633 0.004861 -0.953 0.34060
## .rnorm -0.067111 0.057197 -1.173 0.24066
## education.nonNA -0.014297 0.057762 -0.248 0.80451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2349.3 on 2755 degrees of freedom
## Residual deviance: 2091.7 on 2739 degrees of freedom
## AIC: 2125.7
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.263937008
## 2 0.1 0.346116028
## 3 0.2 0.396074933
## 4 0.3 0.321839080
## 5 0.4 0.192816635
## 6 0.5 0.126637555
## 7 0.6 0.064220183
## 8 0.7 0.037383178
## 9 0.8 0.018867925
## 10 0.9 0.009501188
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.All.X.glm.N
## 1 N 1857
## 2 Y 197
## TenYearCHD.fctr.predict.All.X.glm.Y
## 1 480
## 2 222
## Prediction
## Reference N Y
## N 1857 480
## Y 197 222
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.543541e-01 2.540333e-01 7.378364e-01 7.703300e-01 8.479681e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 2.270390e-27
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.263311878
## 2 0.1 0.349442379
## 3 0.2 0.387858347
## 4 0.3 0.304461942
## 5 0.4 0.209790210
## 6 0.5 0.089795918
## 7 0.6 0.042918455
## 8 0.7 0.008810573
## 9 0.8 0.000000000
## 10 0.9 0.000000000
## 11 1.0 0.000000000## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## TenYearCHD.fctr TenYearCHD.fctr.predict.All.X.glm.N
## 1 N 1006
## 2 Y 110
## TenYearCHD.fctr.predict.All.X.glm.Y
## 1 253
## 2 115
## Prediction
## Reference N Y
## N 1006 253
## Y 110 115
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.553908e-01 2.459645e-01 7.326911e-01 7.770752e-01 8.483827e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 9.119486e-14
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, .rnorm, education.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.426 0.157
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7300008 0.2 0.3960749 0.8534118
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7378364 0.77033 0.1019044 0.7349254
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.3878583 0.7553908
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7326911 0.7770752 0.2459645 2125.724
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004981347 0.05516261
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_glm 2 0 63.726 70.072 6.347
## 3 fit.models_1_bayesglm 3 0 70.073 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, .rnorm, education.nonNA"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
## Loading required package: lme4
##
## arm (Version 1.8-5, built: 2015-05-13)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Lectures/LCTR3_BioLINCC_Framingham
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8783 -0.6025 -0.4334 -0.2929 2.7746
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.833598 0.821378 -9.537 < 2e-16 ***
## age 0.060829 0.007732 7.867 3.62e-15 ***
## sysBP 0.011784 0.004218 2.794 0.00521 **
## prevalentHyp 0.310505 0.158504 1.959 0.05012 .
## diaBP -0.005751 0.007324 -0.785 0.43231
## glucose.nonNA 0.007683 0.002633 2.918 0.00352 **
## totChol.nonNA 0.003199 0.001272 2.515 0.01191 *
## BPMeds.nonNA 0.336633 0.270498 1.244 0.21332
## male 0.511188 0.124579 4.103 4.07e-05 ***
## BMI.nonNA 0.009398 0.014765 0.637 0.52443
## prevalentStroke 1.289595 0.503512 2.561 0.01043 *
## diabetes -0.168695 0.361345 -0.467 0.64061
## cigsPerDay.nonNA 0.016450 0.007106 2.315 0.02062 *
## currentSmoker 0.073509 0.177003 0.415 0.67792
## heartRate.nonNA -0.004633 0.004847 -0.956 0.33913
## .rnorm -0.066528 0.057044 -1.166 0.24351
## education.nonNA -0.015192 0.057693 -0.263 0.79230
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2349.3 on 2755 degrees of freedom
## Residual deviance: 2091.8 on 2739 degrees of freedom
## AIC: 2125.8
##
## Number of Fisher Scoring iterations: 6
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.263937008
## 2 0.1 0.345776031
## 3 0.2 0.394642857
## 4 0.3 0.320916905
## 5 0.4 0.189753321
## 6 0.5 0.126637555
## 7 0.6 0.064220183
## 8 0.7 0.037383178
## 9 0.8 0.018867925
## 10 0.9 0.009501188
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.All.X.bayesglm.N
## 1 N 1857
## 2 Y 198
## TenYearCHD.fctr.predict.All.X.bayesglm.Y
## 1 480
## 2 221
## Prediction
## Reference N Y
## N 1857 480
## Y 198 221
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.539913e-01 2.523581e-01 7.374659e-01 7.699755e-01 8.479681e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 3.765910e-27
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.263311878
## 2 0.1 0.349676226
## 3 0.2 0.386554622
## 4 0.3 0.300000000
## 5 0.4 0.209790210
## 6 0.5 0.089795918
## 7 0.6 0.042918455
## 8 0.7 0.008810573
## 9 0.8 0.000000000
## 10 0.9 0.000000000
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## TenYearCHD.fctr TenYearCHD.fctr.predict.All.X.bayesglm.N
## 1 N 1004
## 2 Y 110
## TenYearCHD.fctr.predict.All.X.bayesglm.Y
## 1 255
## 2 115
## Prediction
## Reference N Y
## N 1004 255
## Y 110 115
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.540431e-01 2.439984e-01 7.313056e-01 7.757707e-01 8.483827e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 4.797852e-14
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, .rnorm, education.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.464 0.204
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7299763 0.2 0.3946429 0.8534118
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7374659 0.7699755 0.1019044 0.7350949
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.3865546 0.7540431
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7313056 0.7757707 0.2439984 2125.755
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004981347 0.05516261
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_bayesglm 3 0 70.073 76.68 6.607
## 4 fit.models_1_rpart 4 0 76.681 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, education.nonNA"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00835 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 2756
##
## CP nsplit rel error
## 1 0.008353222 0 1
##
## Node number 1: 2756 observations
## predicted class=N expected loss=0.1520319 P(node) =1
## class counts: 2337 419
## probabilities: 0.848 0.152
##
## n= 2756
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 2756 419 N (0.8479681 0.1520319) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 2337
## 2 Y 419
## Prediction
## Reference N Y
## N 2337 0
## Y 419 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.479681e-01 0.000000e+00 8.340130e-01 8.611796e-01 8.479681e-01
## AccuracyPValue McnemarPValue
## 5.130333e-01 1.096271e-92
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## TenYearCHD.fctr TenYearCHD.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 1259
## 2 Y 225
## Prediction
## Reference N Y
## N 1259 0
## Y 225 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.483827e-01 0.000000e+00 8.291112e-01 8.662638e-01 8.483827e-01
## AccuracyPValue McnemarPValue
## 5.177806e-01 2.000126e-50
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, education.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 2.658 0.149
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.8392632
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.834013 0.8611796 0.04571195 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8483827
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8291112 0.8662638 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008360189 0.05858524
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_rpart 4 0 76.681 80.967 4.286
## 5 fit.models_1_rf 5 0 80.967 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, education.nonNA"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 2 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: mtry
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 2756 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 5512 matrix numeric
## oob.times 2756 -none- numeric
## classes 2 -none- character
## importance 15 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 2756 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 15 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.263937008
## 2 0.1 0.755635708
## 3 0.2 0.984723854
## 4 0.3 0.991576414
## 5 0.4 0.901703801
## 6 0.5 0.692667707
## 7 0.6 0.388461538
## 8 0.7 0.141906874
## 9 0.8 0.009501188
## 10 0.9 0.000000000
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 2337
## 2 Y 7
## TenYearCHD.fctr.predict.All.X.no.rnorm.rf.Y
## 1 NA
## 2 412
## Prediction
## Reference N Y
## N 2337 0
## Y 7 412
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.974601e-01 9.900811e-01 9.947739e-01 9.989782e-01 8.479681e-01
## AccuracyPValue McnemarPValue
## 5.904678e-183 2.334220e-02
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.26331188
## 2 0.1 0.37486457
## 3 0.2 0.36862745
## 4 0.3 0.32352941
## 5 0.4 0.11718750
## 6 0.5 0.02586207
## 7 0.6 0.00000000
## 8 0.7 0.00000000
## 9 0.8 0.00000000
## 10 0.9 0.00000000
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.OOB"
## TenYearCHD.fctr TenYearCHD.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 734
## 2 Y 52
## TenYearCHD.fctr.predict.All.X.no.rnorm.rf.Y
## 1 525
## 2 173
## Prediction
## Reference N Y
## N 734 525
## Y 52 173
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.111860e-01 1.888575e-01 5.858467e-01 6.360842e-01 8.483827e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 5.825060e-86
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, education.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 15.833 3.72
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9999949 0.3 0.9915764 0.8497826
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9947739 0.9989782 0.0325253 0.711503
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.3748646 0.611186
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5858467 0.6360842 0.1888575
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.003901884 0.03622717# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
#model_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", model_id), select=feats), 1)
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#model_id <- "Rank9.2"; indep_vars_vctr <- c(NULL
# ,"<feat1>"
# ,"<feat1>*<feat2>"
# ,"<feat1>:<feat2>"
# )
# for (method in c("bayesglm")) {
# ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(model_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
# }
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart age, sysBP
## Max.cor.Y.cv.0.cp.0.rpart age, sysBP
## Max.cor.Y.rpart age, sysBP
## Max.cor.Y.glm age, sysBP
## Interact.High.cor.Y.glm age, sysBP, age:sysBP
## Low.cor.X.glm age, sysBP, prevalentHyp, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, .rnorm, education.nonNA
## All.X.glm age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, .rnorm, education.nonNA
## All.X.bayesglm age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, .rnorm, education.nonNA
## All.X.no.rnorm.rpart age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, education.nonNA
## All.X.no.rnorm.rf age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, education.nonNA
## max.nTuningRuns min.elapsedtime.everything
## MFO.myMFO_classfr 0 0.267
## Random.myrandom_classfr 0 0.231
## Max.cor.Y.cv.0.rpart 0 0.586
## Max.cor.Y.cv.0.cp.0.rpart 0 0.487
## Max.cor.Y.rpart 3 1.294
## Max.cor.Y.glm 1 0.971
## Interact.High.cor.Y.glm 1 1.021
## Low.cor.X.glm 1 1.451
## All.X.glm 1 1.426
## All.X.bayesglm 1 2.464
## All.X.no.rnorm.rpart 3 2.658
## All.X.no.rnorm.rf 3 15.833
## min.elapsedtime.final max.auc.fit
## MFO.myMFO_classfr 0.003 0.5000000
## Random.myrandom_classfr 0.002 0.4991605
## Max.cor.Y.cv.0.rpart 0.046 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.043 0.6874657
## Max.cor.Y.rpart 0.061 0.5000000
## Max.cor.Y.glm 0.051 0.6995291
## Interact.High.cor.Y.glm 0.058 0.6995700
## Low.cor.X.glm 0.145 0.7295872
## All.X.glm 0.157 0.7300008
## All.X.bayesglm 0.204 0.7299763
## All.X.no.rnorm.rpart 0.149 0.5000000
## All.X.no.rnorm.rf 3.720 0.9999949
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.1 0.2639370
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.2 0.3675799
## Max.cor.Y.rpart 0.5 0.0000000
## Max.cor.Y.glm 0.2 0.3614679
## Interact.High.cor.Y.glm 0.2 0.3590193
## Low.cor.X.glm 0.2 0.3872679
## All.X.glm 0.2 0.3960749
## All.X.bayesglm 0.2 0.3946429
## All.X.no.rnorm.rpart 0.5 0.0000000
## All.X.no.rnorm.rf 0.3 0.9915764
## max.Accuracy.fit max.AccuracyLower.fit
## MFO.myMFO_classfr 0.8479681 0.8340130
## Random.myrandom_classfr 0.1520319 0.1388204
## Max.cor.Y.cv.0.rpart 0.8479681 0.8340130
## Max.cor.Y.cv.0.cp.0.rpart 0.7989840 0.7835265
## Max.cor.Y.rpart 0.8436122 0.8340130
## Max.cor.Y.glm 0.8497830 0.7308005
## Interact.High.cor.Y.glm 0.8486941 0.7174842
## Low.cor.X.glm 0.8519609 0.7319111
## All.X.glm 0.8534118 0.7378364
## All.X.bayesglm 0.8534118 0.7374659
## All.X.no.rnorm.rpart 0.8392632 0.8340130
## All.X.no.rnorm.rf 0.8497826 0.9947739
## max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.8611796 0.00000000 0.5000000
## Random.myrandom_classfr 0.1659870 0.00000000 0.5169305
## Max.cor.Y.cv.0.rpart 0.8611796 0.00000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8138039 0.24834760 0.6261266
## Max.cor.Y.rpart 0.8611796 0.01359321 0.5000000
## Max.cor.Y.glm 0.7635925 0.03285851 0.7108252
## Interact.High.cor.Y.glm 0.7508120 0.01782193 0.7114853
## Low.cor.X.glm 0.7646567 0.09050139 0.7357374
## All.X.glm 0.7703300 0.10190442 0.7349254
## All.X.bayesglm 0.7699755 0.10190442 0.7350949
## All.X.no.rnorm.rpart 0.8611796 0.04571195 0.5000000
## All.X.no.rnorm.rf 0.9989782 0.03252530 0.7115030
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.1 0.2633119
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.1 0.3320659
## Max.cor.Y.rpart 0.5 0.0000000
## Max.cor.Y.glm 0.2 0.3620981
## Interact.High.cor.Y.glm 0.2 0.3662420
## Low.cor.X.glm 0.2 0.3906511
## All.X.glm 0.2 0.3878583
## All.X.bayesglm 0.2 0.3865546
## All.X.no.rnorm.rpart 0.5 0.0000000
## All.X.no.rnorm.rf 0.1 0.3748646
## max.Accuracy.OOB max.AccuracyLower.OOB
## MFO.myMFO_classfr 0.8483827 0.8291112
## Random.myrandom_classfr 0.1516173 0.1337362
## Max.cor.Y.cv.0.rpart 0.8483827 0.8291112
## Max.cor.Y.cv.0.cp.0.rpart 0.6448787 0.6199261
## Max.cor.Y.rpart 0.8483827 0.8291112
## Max.cor.Y.glm 0.7459569 0.7229984
## Interact.High.cor.Y.glm 0.7318059 0.7084848
## Low.cor.X.glm 0.7540431 0.7313056
## All.X.glm 0.7553908 0.7326911
## All.X.bayesglm 0.7540431 0.7313056
## All.X.no.rnorm.rpart 0.8483827 0.8291112
## All.X.no.rnorm.rf 0.6111860 0.5858467
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.8662638 0.0000000
## Random.myrandom_classfr 0.1708888 0.0000000
## Max.cor.Y.cv.0.rpart 0.8662638 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.6692564 0.1472157
## Max.cor.Y.rpart 0.8662638 0.0000000
## Max.cor.Y.glm 0.7679377 0.2146098
## Interact.High.cor.Y.glm 0.7542060 0.2131223
## Low.cor.X.glm 0.7757707 0.2483375
## All.X.glm 0.7770752 0.2459645
## All.X.bayesglm 0.7757707 0.2439984
## All.X.no.rnorm.rpart 0.8662638 0.0000000
## All.X.no.rnorm.rf 0.6360842 0.1888575
## max.AccuracySD.fit max.KappaSD.fit min.aic.fit
## MFO.myMFO_classfr NA NA NA
## Random.myrandom_classfr NA NA NA
## Max.cor.Y.cv.0.rpart NA NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA NA
## Max.cor.Y.rpart 0.004491870 0.013958451 NA
## Max.cor.Y.glm 0.002818894 0.014355934 2176.886
## Interact.High.cor.Y.glm 0.001007458 0.006917152 2177.511
## Low.cor.X.glm 0.004918549 0.049630177 2124.378
## All.X.glm 0.004981347 0.055162607 2125.724
## All.X.bayesglm 0.004981347 0.055162607 2125.755
## All.X.no.rnorm.rpart 0.008360189 0.058585237 NA
## All.X.no.rnorm.rf 0.003901884 0.036227169 NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 fit.models_1_rf 5 0 80.967 100.559 19.592
## 6 fit.models_1_end 6 0 100.560 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 59.431 100.566 41.135
## 12 fit.models 7 2 100.567 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart age, sysBP
## Max.cor.Y.cv.0.cp.0.rpart age, sysBP
## Max.cor.Y.rpart age, sysBP
## Max.cor.Y.glm age, sysBP
## Interact.High.cor.Y.glm age, sysBP, age:sysBP
## Low.cor.X.glm age, sysBP, prevalentHyp, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, .rnorm, education.nonNA
## All.X.glm age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, .rnorm, education.nonNA
## All.X.bayesglm age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, .rnorm, education.nonNA
## All.X.no.rnorm.rpart age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, education.nonNA
## All.X.no.rnorm.rf age, sysBP, prevalentHyp, diaBP, glucose.nonNA, totChol.nonNA, BPMeds.nonNA, male, BMI.nonNA, prevalentStroke, diabetes, cigsPerDay.nonNA, currentSmoker, heartRate.nonNA, education.nonNA
## max.nTuningRuns max.auc.fit
## MFO.myMFO_classfr 0 0.5000000
## Random.myrandom_classfr 0 0.4991605
## Max.cor.Y.cv.0.rpart 0 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.6874657
## Max.cor.Y.rpart 3 0.5000000
## Max.cor.Y.glm 1 0.6995291
## Interact.High.cor.Y.glm 1 0.6995700
## Low.cor.X.glm 1 0.7295872
## All.X.glm 1 0.7300008
## All.X.bayesglm 1 0.7299763
## All.X.no.rnorm.rpart 3 0.5000000
## All.X.no.rnorm.rf 3 0.9999949
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.1 0.2639370
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.2 0.3675799
## Max.cor.Y.rpart 0.5 0.0000000
## Max.cor.Y.glm 0.2 0.3614679
## Interact.High.cor.Y.glm 0.2 0.3590193
## Low.cor.X.glm 0.2 0.3872679
## All.X.glm 0.2 0.3960749
## All.X.bayesglm 0.2 0.3946429
## All.X.no.rnorm.rpart 0.5 0.0000000
## All.X.no.rnorm.rf 0.3 0.9915764
## max.Accuracy.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.8479681 0.00000000 0.5000000
## Random.myrandom_classfr 0.1520319 0.00000000 0.5169305
## Max.cor.Y.cv.0.rpart 0.8479681 0.00000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.7989840 0.24834760 0.6261266
## Max.cor.Y.rpart 0.8436122 0.01359321 0.5000000
## Max.cor.Y.glm 0.8497830 0.03285851 0.7108252
## Interact.High.cor.Y.glm 0.8486941 0.01782193 0.7114853
## Low.cor.X.glm 0.8519609 0.09050139 0.7357374
## All.X.glm 0.8534118 0.10190442 0.7349254
## All.X.bayesglm 0.8534118 0.10190442 0.7350949
## All.X.no.rnorm.rpart 0.8392632 0.04571195 0.5000000
## All.X.no.rnorm.rf 0.8497826 0.03252530 0.7115030
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.1 0.2633119
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.1 0.3320659
## Max.cor.Y.rpart 0.5 0.0000000
## Max.cor.Y.glm 0.2 0.3620981
## Interact.High.cor.Y.glm 0.2 0.3662420
## Low.cor.X.glm 0.2 0.3906511
## All.X.glm 0.2 0.3878583
## All.X.bayesglm 0.2 0.3865546
## All.X.no.rnorm.rpart 0.5 0.0000000
## All.X.no.rnorm.rf 0.1 0.3748646
## max.Accuracy.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.8483827 0.0000000
## Random.myrandom_classfr 0.1516173 0.0000000
## Max.cor.Y.cv.0.rpart 0.8483827 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.6448787 0.1472157
## Max.cor.Y.rpart 0.8483827 0.0000000
## Max.cor.Y.glm 0.7459569 0.2146098
## Interact.High.cor.Y.glm 0.7318059 0.2131223
## Low.cor.X.glm 0.7540431 0.2483375
## All.X.glm 0.7553908 0.2459645
## All.X.bayesglm 0.7540431 0.2439984
## All.X.no.rnorm.rpart 0.8483827 0.0000000
## All.X.no.rnorm.rf 0.6111860 0.1888575
## inv.elapsedtime.everything inv.elapsedtime.final
## MFO.myMFO_classfr 3.74531835 333.3333333
## Random.myrandom_classfr 4.32900433 500.0000000
## Max.cor.Y.cv.0.rpart 1.70648464 21.7391304
## Max.cor.Y.cv.0.cp.0.rpart 2.05338809 23.2558140
## Max.cor.Y.rpart 0.77279753 16.3934426
## Max.cor.Y.glm 1.02986612 19.6078431
## Interact.High.cor.Y.glm 0.97943193 17.2413793
## Low.cor.X.glm 0.68917988 6.8965517
## All.X.glm 0.70126227 6.3694268
## All.X.bayesglm 0.40584416 4.9019608
## All.X.no.rnorm.rpart 0.37622272 6.7114094
## All.X.no.rnorm.rf 0.06315922 0.2688172
## inv.aic.fit
## MFO.myMFO_classfr NA
## Random.myrandom_classfr NA
## Max.cor.Y.cv.0.rpart NA
## Max.cor.Y.cv.0.cp.0.rpart NA
## Max.cor.Y.rpart NA
## Max.cor.Y.glm 0.0004593717
## Interact.High.cor.Y.glm 0.0004592400
## Low.cor.X.glm 0.0004707261
## All.X.glm 0.0004704280
## All.X.bayesglm 0.0004704212
## All.X.no.rnorm.rpart NA
## All.X.no.rnorm.rf NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.## Warning: Removed 4 rows containing missing values (geom_path).## Warning: Removed 87 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
dev.off()## quartz_off_screen
## 2print(gp)
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 1 MFO.myMFO_classfr 0.8483827 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.8483827 0.5000000 0.0000000
## 5 Max.cor.Y.rpart 0.8483827 0.5000000 0.0000000
## 11 All.X.no.rnorm.rpart 0.8483827 0.5000000 0.0000000
## 9 All.X.glm 0.7553908 0.7349254 0.2459645
## 8 Low.cor.X.glm 0.7540431 0.7357374 0.2483375
## 10 All.X.bayesglm 0.7540431 0.7350949 0.2439984
## 6 Max.cor.Y.glm 0.7459569 0.7108252 0.2146098
## 7 Interact.High.cor.Y.glm 0.7318059 0.7114853 0.2131223
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.6448787 0.6261266 0.1472157
## 12 All.X.no.rnorm.rf 0.6111860 0.7115030 0.1888575
## 2 Random.myrandom_classfr 0.1516173 0.5169305 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 1 NA 0.5
## 3 NA 0.5
## 5 NA 0.5
## 11 NA 0.5
## 9 2125.724 0.2
## 8 2124.378 0.2
## 10 2125.755 0.2
## 6 2176.886 0.2
## 7 2177.511 0.2
## 4 NA 0.1
## 12 NA 0.1
## 2 NA 0.1print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.## Warning: Removed 38 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.auc.OOB - max.Kappa.OOB + min.aic.fit -
## opt.prob.threshold.OOBprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: MFO.myMFO_classfr"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
# if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
# warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
# glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
# }
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8479681 0.1520319
## 2 0.8479681 0.1520319
## 3 0.8479681 0.1520319
## 4 0.8479681 0.1520319
## 5 0.8479681 0.1520319
## 6 0.8479681 0.1520319predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
#stop(here"); #sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)## [1] "in MFO.Classifier$varImp"
## Overall
## .rnorm 0glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance MFO.myMFO_classfr.importance
## .rnorm NaN NaN# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) 
## [1] "Min/Max Boundaries: "
## .rownames TenYearCHD.fctr
## 3636 3636 Y
## 1891 1891 N
## 2 2 N
## 4236 4236 N
## TenYearCHD.fctr.predict.MFO.myMFO_classfr.prob
## 3636 0.1520319
## 1891 0.1520319
## 2 0.1520319
## 4236 0.1520319
## TenYearCHD.fctr.predict.MFO.myMFO_classfr
## 3636 N
## 1891 N
## 2 N
## 4236 N
## TenYearCHD.fctr.predict.MFO.myMFO_classfr.accurate
## 3636 FALSE
## 1891 TRUE
## 2 TRUE
## 4236 TRUE
## TenYearCHD.fctr.predict.MFO.myMFO_classfr.error .label
## 3636 -0.3479681 3636
## 1891 0.0000000 1891
## 2 0.0000000 2
## 4236 0.0000000 4236
## [1] "Inaccurate: "
## .rownames TenYearCHD.fctr
## 1029 1029 Y
## 1033 1033 Y
## 1045 1045 Y
## 1046 1046 Y
## 1052 1052 Y
## 1056 1056 Y
## TenYearCHD.fctr.predict.MFO.myMFO_classfr.prob
## 1029 0.1520319
## 1033 0.1520319
## 1045 0.1520319
## 1046 0.1520319
## 1052 0.1520319
## 1056 0.1520319
## TenYearCHD.fctr.predict.MFO.myMFO_classfr
## 1029 N
## 1033 N
## 1045 N
## 1046 N
## 1052 N
## 1056 N
## TenYearCHD.fctr.predict.MFO.myMFO_classfr.accurate
## 1029 FALSE
## 1033 FALSE
## 1045 FALSE
## 1046 FALSE
## 1052 FALSE
## 1056 FALSE
## TenYearCHD.fctr.predict.MFO.myMFO_classfr.error
## 1029 -0.3479681
## 1033 -0.3479681
## 1045 -0.3479681
## 1046 -0.3479681
## 1052 -0.3479681
## 1056 -0.3479681
## .rownames TenYearCHD.fctr
## 149 149 Y
## 1522 1522 Y
## 2326 2326 Y
## 2391 2391 Y
## 248 248 Y
## 3339 3339 Y
## TenYearCHD.fctr.predict.MFO.myMFO_classfr.prob
## 149 0.1520319
## 1522 0.1520319
## 2326 0.1520319
## 2391 0.1520319
## 248 0.1520319
## 3339 0.1520319
## TenYearCHD.fctr.predict.MFO.myMFO_classfr
## 149 N
## 1522 N
## 2326 N
## 2391 N
## 248 N
## 3339 N
## TenYearCHD.fctr.predict.MFO.myMFO_classfr.accurate
## 149 FALSE
## 1522 FALSE
## 2326 FALSE
## 2391 FALSE
## 248 FALSE
## 3339 FALSE
## TenYearCHD.fctr.predict.MFO.myMFO_classfr.error
## 149 -0.3479681
## 1522 -0.3479681
## 2326 -0.3479681
## 2391 -0.3479681
## 248 -0.3479681
## 3339 -0.3479681
## .rownames TenYearCHD.fctr
## 895 895 Y
## 900 900 Y
## 934 934 Y
## 936 936 Y
## 966 966 Y
## 971 971 Y
## TenYearCHD.fctr.predict.MFO.myMFO_classfr.prob
## 895 0.1520319
## 900 0.1520319
## 934 0.1520319
## 936 0.1520319
## 966 0.1520319
## 971 0.1520319
## TenYearCHD.fctr.predict.MFO.myMFO_classfr
## 895 N
## 900 N
## 934 N
## 936 N
## 966 N
## 971 N
## TenYearCHD.fctr.predict.MFO.myMFO_classfr.accurate
## 895 FALSE
## 900 FALSE
## 934 FALSE
## 936 FALSE
## 966 FALSE
## 971 FALSE
## TenYearCHD.fctr.predict.MFO.myMFO_classfr.error
## 895 -0.3479681
## 900 -0.3479681
## 934 -0.3479681
## 936 -0.3479681
## 966 -0.3479681
## 971 -0.3479681
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 100.567 117.668 17.101
## 13 fit.models 7 3 117.669 NA NAprint(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "TenYearCHD.fctr.predict.MFO.myMFO_classfr.prob"
## [2] "TenYearCHD.fctr.predict.MFO.myMFO_classfr"
## [3] "TenYearCHD.fctr.predict.MFO.myMFO_classfr.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 117.669 126.161 8.492
## 14 fit.data.training 8 0 126.162 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:randomForest':
##
## combine
##
## The following objects are masked from 'package:dplyr':
##
## combine, first, last
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size## [1] "fitting model: Final.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Aggregating results
## Fitting final model on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.8479681 0.1520319
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8479681 0.1520319
## 2 0.8479681 0.1520319
## 3 0.8479681 0.1520319
## 4 0.8479681 0.1520319
## 5 0.8479681 0.1520319
## 6 0.8479681 0.1520319
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## TenYearCHD.fctr TenYearCHD.fctr.predict.Final.myMFO_classfr.N
## 1 N 2337
## 2 Y 419
## Prediction
## Reference N Y
## N 2337 0
## Y 419 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.479681e-01 0.000000e+00 8.340130e-01 8.611796e-01 8.479681e-01
## AccuracyPValue McnemarPValue
## 5.130333e-01 1.096271e-92## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only## model_id model_method feats max.nTuningRuns
## 1 Final.myMFO_classfr myMFO_classfr .rnorm 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.756 0.004 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8479683
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.834013 0.8611796 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0005331122 0rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 126.162 129.436 3.274
## 15 fit.data.training 8 1 129.437 NA NAglb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.5## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8479681 0.1520319
## 2 0.8479681 0.1520319
## 3 0.8479681 0.1520319
## 4 0.8479681 0.1520319
## 5 0.8479681 0.1520319
## 6 0.8479681 0.1520319sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)## [1] "in MFO.Classifier$varImp"
## Overall
## .rnorm 0glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## MFO.myMFO_classfr.importance importance
## .rnorm NaN NaN
## Final.myMFO_classfr.importance
## .rnorm NaNif (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) 
## [1] "Min/Max Boundaries: "
## .rownames TenYearCHD.fctr
## 1 1 N
## 2485 2485 N
## 4240 4240 N
## 924 924 N
## TenYearCHD.fctr.predict.Final.myMFO_classfr.prob
## 1 0.1520319
## 2485 0.1520319
## 4240 0.1520319
## 924 0.1520319
## TenYearCHD.fctr.predict.Final.myMFO_classfr
## 1 N
## 2485 N
## 4240 N
## 924 N
## TenYearCHD.fctr.predict.Final.myMFO_classfr.accurate
## 1 TRUE
## 2485 TRUE
## 4240 TRUE
## 924 TRUE
## TenYearCHD.fctr.predict.Final.myMFO_classfr.error .label
## 1 0 1
## 2485 0 2485
## 4240 0 4240
## 924 0 924
## [1] "Inaccurate: "
## .rownames TenYearCHD.fctr
## 1028 1028 Y
## 1072 1072 Y
## 1077 1077 Y
## 108 108 Y
## 109 109 Y
## 1090 1090 Y
## TenYearCHD.fctr.predict.Final.myMFO_classfr.prob
## 1028 0.1520319
## 1072 0.1520319
## 1077 0.1520319
## 108 0.1520319
## 109 0.1520319
## 1090 0.1520319
## TenYearCHD.fctr.predict.Final.myMFO_classfr
## 1028 N
## 1072 N
## 1077 N
## 108 N
## 109 N
## 1090 N
## TenYearCHD.fctr.predict.Final.myMFO_classfr.accurate
## 1028 FALSE
## 1072 FALSE
## 1077 FALSE
## 108 FALSE
## 109 FALSE
## 1090 FALSE
## TenYearCHD.fctr.predict.Final.myMFO_classfr.error
## 1028 -0.3479681
## 1072 -0.3479681
## 1077 -0.3479681
## 108 -0.3479681
## 109 -0.3479681
## 1090 -0.3479681
## .rownames TenYearCHD.fctr
## 2646 2646 Y
## 3078 3078 Y
## 3144 3144 Y
## 377 377 Y
## 4120 4120 Y
## 95 95 Y
## TenYearCHD.fctr.predict.Final.myMFO_classfr.prob
## 2646 0.1520319
## 3078 0.1520319
## 3144 0.1520319
## 377 0.1520319
## 4120 0.1520319
## 95 0.1520319
## TenYearCHD.fctr.predict.Final.myMFO_classfr
## 2646 N
## 3078 N
## 3144 N
## 377 N
## 4120 N
## 95 N
## TenYearCHD.fctr.predict.Final.myMFO_classfr.accurate
## 2646 FALSE
## 3078 FALSE
## 3144 FALSE
## 377 FALSE
## 4120 FALSE
## 95 FALSE
## TenYearCHD.fctr.predict.Final.myMFO_classfr.error
## 2646 -0.3479681
## 3078 -0.3479681
## 3144 -0.3479681
## 377 -0.3479681
## 4120 -0.3479681
## 95 -0.3479681
## .rownames TenYearCHD.fctr
## 963 963 Y
## 975 975 Y
## 977 977 Y
## 979 979 Y
## 98 98 Y
## 986 986 Y
## TenYearCHD.fctr.predict.Final.myMFO_classfr.prob
## 963 0.1520319
## 975 0.1520319
## 977 0.1520319
## 979 0.1520319
## 98 0.1520319
## 986 0.1520319
## TenYearCHD.fctr.predict.Final.myMFO_classfr
## 963 N
## 975 N
## 977 N
## 979 N
## 98 N
## 986 N
## TenYearCHD.fctr.predict.Final.myMFO_classfr.accurate
## 963 FALSE
## 975 FALSE
## 977 FALSE
## 979 FALSE
## 98 FALSE
## 986 FALSE
## TenYearCHD.fctr.predict.Final.myMFO_classfr.error
## 963 -0.3479681
## 975 -0.3479681
## 977 -0.3479681
## 979 -0.3479681
## 98 -0.3479681
## 986 -0.3479681
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "TenYearCHD.fctr.predict.Final.myMFO_classfr.prob"
## [2] "TenYearCHD.fctr.predict.Final.myMFO_classfr"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 129.437 144.132 14.695
## 16 predict.data.new 9 0 144.132 NA NAStep 9.0: predict data new
# Compute final model predictions
# sav_newobs_df <- glb_newobs_df
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(glb_newobs_df, mdl_id = glb_fin_mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.5## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8479681 0.1520319
## 2 0.8479681 0.1520319
## 3 0.8479681 0.1520319
## 4 0.8479681 0.1520319
## 5 0.8479681 0.1520319
## 6 0.8479681 0.1520319if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) 
## [1] "Min/Max Boundaries: "
## .rownames TenYearCHD.fctr
## 3636 3636 Y
## 1891 1891 N
## 2 2 N
## 4236 4236 N
## TenYearCHD.fctr.predict.Final.myMFO_classfr.prob
## 3636 0.1520319
## 1891 0.1520319
## 2 0.1520319
## 4236 0.1520319
## TenYearCHD.fctr.predict.Final.myMFO_classfr
## 3636 N
## 1891 N
## 2 N
## 4236 N
## TenYearCHD.fctr.predict.Final.myMFO_classfr.accurate
## 3636 FALSE
## 1891 TRUE
## 2 TRUE
## 4236 TRUE
## TenYearCHD.fctr.predict.Final.myMFO_classfr.error .label
## 3636 -0.3479681 3636
## 1891 0.0000000 1891
## 2 0.0000000 2
## 4236 0.0000000 4236
## [1] "Inaccurate: "
## .rownames TenYearCHD.fctr
## 1029 1029 Y
## 1033 1033 Y
## 1045 1045 Y
## 1046 1046 Y
## 1052 1052 Y
## 1056 1056 Y
## TenYearCHD.fctr.predict.Final.myMFO_classfr.prob
## 1029 0.1520319
## 1033 0.1520319
## 1045 0.1520319
## 1046 0.1520319
## 1052 0.1520319
## 1056 0.1520319
## TenYearCHD.fctr.predict.Final.myMFO_classfr
## 1029 N
## 1033 N
## 1045 N
## 1046 N
## 1052 N
## 1056 N
## TenYearCHD.fctr.predict.Final.myMFO_classfr.accurate
## 1029 FALSE
## 1033 FALSE
## 1045 FALSE
## 1046 FALSE
## 1052 FALSE
## 1056 FALSE
## TenYearCHD.fctr.predict.Final.myMFO_classfr.error
## 1029 -0.3479681
## 1033 -0.3479681
## 1045 -0.3479681
## 1046 -0.3479681
## 1052 -0.3479681
## 1056 -0.3479681
## .rownames TenYearCHD.fctr
## 1046 1046 Y
## 1879 1879 Y
## 2337 2337 Y
## 2931 2931 Y
## 895 895 Y
## 934 934 Y
## TenYearCHD.fctr.predict.Final.myMFO_classfr.prob
## 1046 0.1520319
## 1879 0.1520319
## 2337 0.1520319
## 2931 0.1520319
## 895 0.1520319
## 934 0.1520319
## TenYearCHD.fctr.predict.Final.myMFO_classfr
## 1046 N
## 1879 N
## 2337 N
## 2931 N
## 895 N
## 934 N
## TenYearCHD.fctr.predict.Final.myMFO_classfr.accurate
## 1046 FALSE
## 1879 FALSE
## 2337 FALSE
## 2931 FALSE
## 895 FALSE
## 934 FALSE
## TenYearCHD.fctr.predict.Final.myMFO_classfr.error
## 1046 -0.3479681
## 1879 -0.3479681
## 2337 -0.3479681
## 2931 -0.3479681
## 895 -0.3479681
## 934 -0.3479681
## .rownames TenYearCHD.fctr
## 895 895 Y
## 900 900 Y
## 934 934 Y
## 936 936 Y
## 966 966 Y
## 971 971 Y
## TenYearCHD.fctr.predict.Final.myMFO_classfr.prob
## 895 0.1520319
## 900 0.1520319
## 934 0.1520319
## 936 0.1520319
## 966 0.1520319
## 971 0.1520319
## TenYearCHD.fctr.predict.Final.myMFO_classfr
## 895 N
## 900 N
## 934 N
## 936 N
## 966 N
## 971 N
## TenYearCHD.fctr.predict.Final.myMFO_classfr.accurate
## 895 FALSE
## 900 FALSE
## 934 FALSE
## 936 FALSE
## 966 FALSE
## 971 FALSE
## TenYearCHD.fctr.predict.Final.myMFO_classfr.error
## 895 -0.3479681
## 900 -0.3479681
## 934 -0.3479681
## 936 -0.3479681
## 966 -0.3479681
## 971 -0.3479681
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
submit_fname <- paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv")
write.csv(submit_df, submit_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.5print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: MFO.myMFO_classfr"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.myMFO_classfr"print(dim(glb_fitobs_df))## [1] 2756 27print(dsp_models_df)## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 1 MFO.myMFO_classfr 0.8483827 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.8483827 0.5000000 0.0000000
## 5 Max.cor.Y.rpart 0.8483827 0.5000000 0.0000000
## 11 All.X.no.rnorm.rpart 0.8483827 0.5000000 0.0000000
## 9 All.X.glm 0.7553908 0.7349254 0.2459645
## 8 Low.cor.X.glm 0.7540431 0.7357374 0.2483375
## 10 All.X.bayesglm 0.7540431 0.7350949 0.2439984
## 6 Max.cor.Y.glm 0.7459569 0.7108252 0.2146098
## 7 Interact.High.cor.Y.glm 0.7318059 0.7114853 0.2131223
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.6448787 0.6261266 0.1472157
## 12 All.X.no.rnorm.rf 0.6111860 0.7115030 0.1888575
## 2 Random.myrandom_classfr 0.1516173 0.5169305 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 1 NA 0.5
## 3 NA 0.5
## 5 NA 0.5
## 11 NA 0.5
## 9 2125.724 0.2
## 8 2124.378 0.2
## 10 2125.755 0.2
## 6 2176.886 0.2
## 7 2177.511 0.2
## 4 NA 0.1
## 12 NA 0.1
## 2 NA 0.1if (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_vars)) {
stop("not implemented yet")
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}
if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_vars)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
} ## [1] "MFO.myMFO_classfr OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 1259 0
## Y 225 0
## [1] "Final.myMFO_classfr new confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 1259 0
## Y 225 0dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## MFO.myMFO_classfr.importance importance
## .rnorm NaN NaN
## Final.myMFO_classfr.importance
## .rnorm NaN# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
if (length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
if (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
if (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)## Warning in rm(submit_df, tmp_OOBobs_df): object 'tmp_OOBobs_df' not found# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 144.132 162.716 18.584
## 17 display.session.info 10 0 162.716 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 59.431 100.566 41.135
## 10 fit.models 7 0 31.473 59.430 27.958
## 16 predict.data.new 9 0 144.132 162.716 18.584
## 12 fit.models 7 2 100.567 117.668 17.101
## 15 fit.data.training 8 1 129.437 144.132 14.695
## 2 inspect.data 2 0 7.882 17.703 9.822
## 7 manage.missing.data 4 1 21.321 30.357 9.036
## 13 fit.models 7 3 117.669 126.161 8.492
## 14 fit.data.training 8 0 126.162 129.436 3.274
## 3 scrub.data 2 1 17.704 19.657 1.953
## 5 extract.features 3 0 19.717 21.030 1.313
## 8 select.features 5 0 30.357 31.135 0.778
## 1 import.data 1 0 7.369 7.882 0.513
## 9 partition.data.training 6 0 31.135 31.473 0.338
## 6 cluster.data 4 0 21.031 21.321 0.290
## 4 transform.data 2 2 19.657 19.717 0.060
## duration
## 11 41.135
## 10 27.957
## 16 18.584
## 12 17.101
## 15 14.695
## 2 9.821
## 7 9.036
## 13 8.492
## 14 3.274
## 3 1.953
## 5 1.313
## 8 0.778
## 1 0.513
## 9 0.338
## 6 0.290
## 4 0.060
## [1] "Total Elapsed Time: 162.716 secs"
## R version 3.2.0 (2015-04-16)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] gdata_2.16.1 randomForest_4.6-10 arm_1.8-5
## [4] lme4_1.1-8 Matrix_1.2-1 MASS_7.3-41
## [7] rpart.plot_1.5.2 rpart_4.1-9 ROCR_1.0-7
## [10] gplots_2.17.0 mice_2.22 Rcpp_0.11.6
## [13] dplyr_0.4.2 plyr_1.8.3 sqldf_0.4-10
## [16] RSQLite_1.0.0 DBI_0.3.1 gsubfn_0.6-6
## [19] proto_0.3-10 reshape2_1.4.1 caTools_1.17.1
## [22] doMC_1.3.3 iterators_1.0.7 foreach_1.4.2
## [25] doBy_4.5-13 survival_2.38-2 caret_6.0-47
## [28] ggplot2_1.0.1 lattice_0.20-31
##
## loaded via a namespace (and not attached):
## [1] class_7.3-12 gtools_3.5.0 assertthat_0.1
## [4] digest_0.6.8 R6_2.0.1 BradleyTerry2_1.0-6
## [7] chron_2.3-47 coda_0.17-1 evaluate_0.7
## [10] e1071_1.6-4 lazyeval_0.1.10 minqa_1.2.4
## [13] SparseM_1.6 car_2.0-25 nloptr_1.0.4
## [16] rmarkdown_0.7 labeling_0.3 splines_3.2.0
## [19] stringr_1.0.0 munsell_0.4.2 compiler_3.2.0
## [22] mgcv_1.8-6 htmltools_0.2.6 nnet_7.3-9
## [25] codetools_0.2-11 brglm_0.5-9 bitops_1.0-6
## [28] nlme_3.1-120 gtable_0.1.2 magrittr_1.5
## [31] formatR_1.2 scales_0.2.5 KernSmooth_2.23-14
## [34] stringi_0.5-2 RColorBrewer_1.1-2 tools_3.2.0
## [37] abind_1.4-3 pbkrtest_0.4-2 yaml_2.1.13
## [40] colorspace_1.2-6 knitr_1.10.5 quantreg_5.11